{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CART\n",
    "\n",
    "In this notebook we give a take on the CART algorithm used to train decision trees. The aim of the notebook is to understand how the learning of the tree happens. To do so, we write a highly-recursive, high-memory class `RTree` that fits a decision tree.\n",
    "\n",
    "The objective function for a _greedy_ decision tree model is given by $(i, j)$ such that \n",
    "\n",
    "$$\n",
    "    \\min_{i, j}\\left[\\sum_{\\{i | x_i \\in R_1(j, s)\\}}(y_i - \\hat c_1)^2 + \\sum_{\\{i | x_i \\in R_2(j, s)\\}}(y_i - \\hat c_2)^2\\right]\n",
    "$$\n",
    "\n",
    "Where,\n",
    "\n",
    "$$\n",
    "    \\hat c_k = \\frac{1}{N_k}\\sum_{\\{n|x_n\\in R_k(j, s)\\}} x_n\n",
    "$$\n",
    "\n",
    "That is, our search algorithm's goal is to find, for each input dimension $j$, the value $s$ that minimizes the error term\n",
    "\n",
    "> Tree based models work by partitioning the input space into cuboid regios, whose edges are aligned with the axes, and then **assigning model** to each region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import seed, multivariate_normal\n",
    "from matplotlib import style\n",
    "from tree import Node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = \"retina\"\n",
    "style.use(\"seaborn-white\")\n",
    "plt.rcParams.update({'font.size': 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "class RTree:\n",
    "    def __init__(self, X, y, depth):\n",
    "        \"\"\"\n",
    "        X: Numpy array\n",
    "            Dataset of shape (N, K)\n",
    "        y: Numpy array\n",
    "            Dataset of shape (N,)\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "        self.left = None\n",
    "        self.right = None\n",
    "        self.info = None\n",
    "        self.depth = depth\n",
    "        self.trained_subtree = False\n",
    "    \n",
    "    @property\n",
    "    def nterminal_nodes(self):\n",
    "        return 2 ** self.depth\n",
    "        \n",
    "    def fit(self):\n",
    "        pass\n",
    "    \n",
    "    def _prune(self):\n",
    "        pass\n",
    "    \n",
    "    def predict(self, x):\n",
    "        pass\n",
    "    \n",
    "    def fit_subtrees(self):\n",
    "        \"\"\"\n",
    "        Fit the current node of the tree, i.e., store at\n",
    "        self.info (s, E, j) (cutoff point, error term, input dimension)\n",
    "        and prepare the left and right subtree to be fit.\n",
    "        \n",
    "        If self.depth == 0 we stop the recursive process.\n",
    "        Meaning we've reached a terminal node\n",
    "        \"\"\"\n",
    "        self.find_min_err()\n",
    "        s, _, j = self.info\n",
    "        # Elements belonging to R1\n",
    "        R1 = self.Ri(j, s)\n",
    "        yR1 = self.y[R1]\n",
    "        XR1 = self.X[R1, :]\n",
    "        # Elements belonging to R2\n",
    "        R2 = ~R1\n",
    "        yR2 = self.y[R2]\n",
    "        XR2 = self.X[R2, :]\n",
    "        \n",
    "        # Stop splitting the input dimension if there is no element\n",
    "        # inside a region\n",
    "        empty_subset = True if len(yR1) < 1 or len(yR2) < 1 else False\n",
    "        if not empty_subset and self.depth >= 1:\n",
    "            self.left = RTree(XR1, yR1, self.depth - 1)\n",
    "            self.right = RTree(XR2, yR2, self.depth - 1)\n",
    "\n",
    "            self.left.fit_subtrees()\n",
    "            self.right.fit_subtrees()\n",
    "\n",
    "    \n",
    "    def find_min_err(self):\n",
    "        \"\"\"\n",
    "        For the current subtree, find the cutoffpoint s'\n",
    "        the error term that minimizes the criteria and\n",
    "        the input dimension j that results in the lowest\n",
    "        mean of squares error.\n",
    "        \"\"\"\n",
    "        if not self.trained_subtree:\n",
    "            # cutoff point, error value, dim\n",
    "            dimerr = [(*self._min_err_j(j), j) for j in range(self.X.shape[1])]\n",
    "            self.info = min(dimerr, key=lambda v: v[1])\n",
    "            self.data = \"*\"\n",
    "            self.trained_subtree = True\n",
    "        else:\n",
    "            warnings.warn(\"Subtree already trained. You can find the cutoffpoint, err, j at RTree.data\")\n",
    "        \n",
    "    def _min_err_j(self, j):\n",
    "        \"\"\"\n",
    "        Considering the j-th input dimension of our traning dataset,\n",
    "        find the cutoff point s' such that minimizes the objetive error function\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        (float, float):\n",
    "            Return a tuple with two elements: the optimal cutoffpoint s'\n",
    "            and the error value when evaluating self.Ri(j, s')\n",
    "        \"\"\"\n",
    "        Xj = self.X[:, j]\n",
    "        smin, smax = Xj.min(), Xj.max() * 0.99\n",
    "        sdom = np.linspace(smin, smax, 100)\n",
    "        sprime_tuple = min([(s, self.E(j, s)) for s in sdom], key=lambda v: v[1])\n",
    "        # cutoff point, error value\n",
    "        return sprime_tuple\n",
    "        \n",
    "    def Ri(self, j, s):\n",
    "        \"\"\"\n",
    "        Compute whether each of the observations belong to either\n",
    "        R1 or R2.\n",
    "        We assign an observation x to be in R1 if x_j <= s; otherwise we\n",
    "        assign it to R2. In other words, we define Ri ⊆ X as:\n",
    "            \n",
    "        R1 = {x | x_j <= s};\n",
    "        R2 = X - R1\n",
    "        \n",
    "        Note that the negative of the function, i.e., ~self.Ri(j, s)\n",
    "        will return true for which values in the training dataset belong\n",
    "        to R2. \n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        j: int\n",
    "            the j-th input dimension of X\n",
    "        s: float\n",
    "            cutoff point\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        np.array(N,) of dtype bool:\n",
    "            Return a boolean numpy array of the training dataset\n",
    "            for which traning examples belong to R1.\n",
    "        \"\"\"\n",
    "        return self.X[:, j] <= s\n",
    "    \n",
    "    @staticmethod\n",
    "    def RSS(y):\n",
    "        \"\"\"\n",
    "        Compute the mean residual sum of squares error\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        y: np.array(N,)\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        float: mean residual error\n",
    "        \"\"\"\n",
    "        rss = 0 if len(y) == 0 else np.mean((y - y.mean()) ** 2)\n",
    "        return rss\n",
    "    \n",
    "    def E(self, j, s):\n",
    "        \"\"\"\n",
    "        Compute the objective error function for a decision tree model\n",
    "        considering an index j in [0, .., D -1] and a cutoff point s.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        j: int\n",
    "            The dimension to evaluate\n",
    "        s: float\n",
    "            The cutoff point\n",
    "        \"\"\"\n",
    "        yR1 = self.y[self.Ri(j, s)] # Elements belonging to R1\n",
    "        yR2 = self.y[~self.Ri(j, s)] # Elements belonging to R2\n",
    "        return self.RSS(yR1) + self.RSS(yR2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed(314)\n",
    "D1 = multivariate_normal([-1 , 1], np.identity(2) * 0.2, 50)\n",
    "D2 = multivariate_normal([ 1 ,-1], np.identity(2) * 0.2, 50)\n",
    "D3 = multivariate_normal([-1 ,-1], np.identity(2) * 0.2, 50)\n",
    "D4 = multivariate_normal([ 1,  1], np.identity(2) * 0.2, 50)\n",
    "y1 = np.random.randn(50) + 3\n",
    "y2 = np.random.randn(50) - 4\n",
    "y3 = np.random.randn(50) - 8\n",
    "y4 = np.random.randn(50) + 10\n",
    "\n",
    "D = np.r_[D1,D2, D3, D4]\n",
    "y = np.r_[y1, y2, y3, y4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under this first version of the `RTree` class, we can fit a depth-2 regression tree as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = RTree(D, y, 2)\n",
    "tree.fit_subtrees()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a depth-2 regression treee we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.nterminal_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under `tree.data`, we can find the cutoff point, the error term and the objetive dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.16861316808986837, 22.428994493267734, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results above show that, for a depth-1 tree, the _locally_ optimum cutoffpoint would be at around `0.17` along the y-axis. If a new observation `x` were to be such that its second entry is less than `0.17` (`x[1] <= 0.17`), we estimate a value `yhat` of "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.748803773471802"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.left.y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, if `x[1] > 0.17`, we estimate `yhat` as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.476684221043483"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.right.y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> Tree based model can be viewed as a model combination method in which only one model is responsible for making predictions at any given point in input space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fd55a4ae410>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 267,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(*D.T, c=y, cmap=\"viridis\")\n",
    "plt.xlabel(\"$x_1$\")\n",
    "plt.ylabel(\"$x_2$\")\n",
    "plt.axhline(y=tree.info[0], c=\"tab:gray\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a depth-2 regression tree (as in this example), and given a new observation `x`. From its first level we know that the first cutoffpoint happens at its second dimension with value `0.17`.\n",
    "\n",
    "If `x[1] <= 0.17` then we consider the **second** cutoffpoint "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.07028726517232764, 8.397848955410351, 0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.left.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second cutoff point, for `x[1] <= 0.17`, happens that the first input dimension at around `-0.07`.  \n",
    "Hence, for `x[1] <= 0.17 and x[0] <= -0.07`, we estimate `yhat` as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.8150199660642"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.left.left.y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, for `x[1] <= 0.17 and x[0] > -0.07` we estimate `yhat` as "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.6825875808794066"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.left.right.y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly for `x[1] > 0.17 and x[0] <= -0.07` we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8574605757872753"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.right.left.y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, for `x[1] > 0.17 and x[0] > -0.07` we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.095907866299692"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.right.right.y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus partitioning $\\mathbb{R}^2$ as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fd55aa66310>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/i6mNAvqKkpZ6bK4pU9RnZfEuLBs2MeSNXggh0VEQdzHyzXNR5diGSsd38IhWqAUjMgzjMcA8GyoZE0e1qmQk6WehybVR0bUzzFeFGjYhRCYpTOvI9waP58z+Fjpd8JLdtjYd+0UTJfLdaGxsRFVVVbTDOKe8d/yA4j5HW+pxoLEaY1ICPwojfZfF40KzxwmdoEaawUyTnPsRxgRkm6Yj2zQ95HNkx9+oKJHXqbKQYpgd8vUIIf1Px1F8OQODbaP3oYz+RwIl8t1ITk7GkSNHoh3GOaXSHtojqiqHlRL5fkaUJGyoPIqVR3fi25rS9vezjPG4ccgELB48Hqn64I8/Sf+XYpiDNOOlqHN8IqO1CsNS/wjG6GOPkEhqnewarg2hJPR8f9fAOpbTuN3uoO3bRuJjpYya/qKRmKAO8c6WRmj7F4vHhR9vehffdUjg21Q5LHh839d4rmgLnpl1Lc7LyO/1+EhsYYxhRNrfwepVAZeiFJgehamPI9kwqxejI+TcxBHGya5hOUtgRqMRjDFwzuF0OoO2dzgcAKBoVZxIio3nAuScVxCfHFq/uND6kdjjEUXcufHtbpP4jqxeN27/eg32NpzqncBITBOYFiNSH8fYjFeQapwP4MxKVlpVGgYm/B+m5nyGVNPF0QuSEBKzBEFAZmYmAAQtq3a5XGhubgYApKeHNuE+3GhEnsSEaweNwT/2fgORy7//npyWi8EJKRGMivSmNcf3YGttuay2LtGHh7Z/gg/m30aTnQkYY0gyzECSYQZEyQWv1ASBaaEREsH8rENPCIkUFsadXRl6Y1x+yJAhqKqqQkVFBTjnfj9XysvPfEYNHjw44nHJQSPyJCZkGuNwad4IRX1uHjE5QtGQ3sY5x8qjuxT1OdhUgz00Kk/OohL00KuzoFWlUBJPCJFl/PjxAIDm5maUlJT4bbd9+/b2rydPjo0chBJ5EjN+P2Ue8uOSZLVdPGQcLleY+JPYdaSlDsUtdYr7vV+qfLUjQgghkdNaI8/C8uqNGnkAuOSSS9q/fvfdd/22azuWnJyMSZNiYx8bSuRJzEjWG7Fm3lJMTvO/Dr/AGO4YOQ1/mnYplVT0I7VOW0j9qp3WMEdCCCHkXDNkyBBMnToVALBy5Urs2LGjS5vnn38eBw60Dh4tWbIEGk3w/S56A9XIk5iSZjBjzbyl2FlXiZXFu7CnvhJO0YcknQEX5w7FDUMnINsUH+0wSZipQrwp0wpUOkEIiW0S94JBBcbOjbFTDoRx+cne89vf/hYLFiyAx+PBbbfdhttvvx3nn38+XC4X1q5diw8//BAAMGjQINx22229GFlglMiTmMMYw+T0XExOpx1yzxWD4lNCmtI0OD41EuEQQkiPWD2lKLW8hQrbOnikJgAMZk0B8uO+jwFxV0Or6s8DUgxSmJafRC/uEDt06FA89dRTuOeee+BwOPD000/j6aef7tRm4MCBeO6552A0GnstrmAokSckhji9Xmw6WYYGpwMaQYVRaekYkZoW7bAiLssYj9nZQ/DlKf+TjM4mgOG6QeMiGBUhhCjDOcfhpqdR3Pz82Udg8x7HgcbHcbjpGUzO+BsyjOdHJUbi34UXXoh169bhpZdewjfffIPq6mowxlBQUID58+fjpptuiqkkHqBEnpCYUO9w4Jmd2/BW0QFYztpZbmJmFm6fMBmXDRkWpeh6xy3DpihK5C8ZMJzKrAghMaWo6UkcbX4pYBsft2Nr9d04L+tppBmm9VJkvSfWSmvuuusu3HXXXbLbZ2Vl4cEHH8SDDz4YhqtH3rlRsEVIDCttbsL333odL+7Z2SWJB4Bd1VX4v08+xJ82fQ2uYJ39vmZWVgH+b9QMWW0Hx6fgkSmXBG9ICCG9pMl9MGgS34ZDxK7ahyBxb4SjIv0djciTgDjn2NNUio8rd6Pa2QzGGAYYU3B17mQMT8iOdnh9nsXtxs3vv4MKiyVo2+d370Ca0YTlE2Nj7dpI+PmYCxGv0eMf+7+BS/R12+b8jHz8c8bVSNLF1uNNQsi57UTLakXtXWIdqu1fIdvcv3Yd5qeXjgzXuUhglMgTvw63VOIP+9/BUWt1p/e34CjeKt+C8UkD8bsxC5Frot1VQ7X64H6UW1pkt39y+xYsGTMOxhhZ9ircGGNYXjgdiwaPw7sn9uPTk0fQ6HZAr1JjbHIWlgydhJFJGdEOkxBCOhElNyrt6xX3K7d90O8SeQBh3NmVBEOJPOnWvqZy/GT7S3CKHr9t9jSV4bYtz+D56XdioIlWD1FK4hyvH9irqI/V48YHxUW4ftTYCEUVGxK0Btw6fCpuHT412qEQQkhQbqkREu9aGhmMw0u7U5OeoUSedGH3uXHvrhUBk/g2TR477t25Am/O+ilUMb5GrihJ+PLUMeyuq4RT9CJRa8DFA4ahMCk9KvGcaGpEWUuz4n5fnDje7xN5QgjpS1jIyyT2v9IRDgYxTMtP8n74/Qk3SuRJF+sqd6PJY5fdvtReh+/qijErfUQEowod5xyvHtmJ54u24pS9cy36P/ZtxOS0XPxqwmxMSR/Qq3G1dDOxVQ6LJ7R+hBBCIkOnSoGKGSByp6J+Jk3vfu6Q/ie2h1BJVLx7cpviPu+Ub41AJD3HOceDWz/F73d83iWJb7OjrgI3bliF9eVHejW2UOvcjer+WR9PCCF9lcA0GGC+QnG/gXHfj0A00SeBheVFgqMRedKJTxJRctbkVjmKLVURiKbnnju0FW+U7AnazidJuHvT+3j/0lswopdKbQYlJSPFYECDU9kIztQc2vGWkEC8kh3l1nVocO2Hj7ugFeKRbboAWcbzwZgq2uGRfqogYTFKrW9D7urnRnVOv9wUigNhLK0hwdCIPOnEx8WQ+nmk2FsL1y368NyhLbLbeyQRLxQpfxoRKq1KhcUjxyjqoxEEXDdydIQiIqRvk7gP+xuexEell2BX/V9QZvsYlfb/4YT1PXxb/TOsK78GJ22fRTtM0k/Fa4dgZPLdstoKTIfJ6X+hG0vSY5TIk050ggYGlVZxvyStOQLR9Mwn5YfR6G4b7eZQqUVodD5o9V5odD4IKgln3+9/WHoITW5Hr8W4dOx4xGnlf79vGD0WKQZaP52Qs0nchy019+Nw8yvw8e5/hx2+U9hS82uUKFzvmxC5hibeitEpvwQLUPCgU6Xi/KznkaRXNpDTV3AwiBDC8qLJrsFRIk86YYxhbqbyEd+LsmLvD9L22goAHGqNCL3JA53BB41WhFojQaMVoTd6oTd5oVKfeQrhkUTsre+9MqEscxyeu+IaWXXv3xtYgAdnzo58UIT0QYeankel/UtZbXfX/x11zl0RjoicqwYnLMG8vPUoTPoJzJpBUDMztEISUvQTMTHtUVyctw7Jelp5jIQH1ciTLhbmTcdHlfI/5ATGcE3ulAhGFBqbxw2NToRG679cSBA4dAYfPC4On7f118HuC77sZjhNzxmAtxZejz9t+hrfVpR3OZ6k12PpmPG4a8p0aFT0GJaQs/kkp8JRdo7i5pVIM0yMWEzk3KZXp2BY0g8wLOkH0Q4lKiTakbXXUCJPuhiVmIurcifhg4qdstrfPvh7yDAkRDgq5Rp8LdBoRXAOsCB/U7R6EZIkQBIFxGv1vRNgByPT0rHy+9fhWFMj1pUUo95hh06lRmFqGi4bMgw6dWz9qja7nVhfcQS1DhvUgoChCamYnT0EaoEe8pHeV2H7HF7JqqjPKcdGOHzVMKozQ7qm3deASvtOeCQ7tIIBWcZxiNNkhXQuQggJVWxlByRm/HrUNXCLPqyvCrzz6NKCWVg+ZG4vRSWfyCWUu2oBBE/i22i0Pmh9cZiUlhPByAIbnJSMu6ZMj9r1g6lxWPH4vq/xYdkhuEVfp2OZhjgsHTYJdxROp4SegHOOBtdWlFvWoMm9GyJ3QSPEI8M4G3lxi2HWDgrbtRrdB0PoJaHJfVhxIt/gKsGuxhU4Yd0Ijo5P+xjyTNMxIXkJMo2xV2pISG9pq5EP17lIYJTIk26pBRUeGbcIczNH4+3yrdjWUNJ+TADDBRmFuC5vOqamDolilP59V3sMDR6b7PacAyo1x7zswTCqlU/2PRcctzRg6RerUO3ofuSz2mnFY3u/ws66Cvx31rXQUhnQOcvlq8XO2p+ixb2/0/s+yYpSy+sotbyOXPMCjE79DQTW830RfNwVUj9RUra5WrltCz479VuIvLt+HOX2zThp34rZmfdhWMIlIcVESF/HOSCFa/lJWn8yKErkiV+MMXwvcxS+lzkKNc4W1LhaIDCGLEMiUnRx0Q4voN2NJxW1bxu1H5WaFoFo+j6714PbvlrtN4nv6MtTJXh4x3r8adplvRAZiTVusRFbqm6Bw9d1vkdHFbZ34ZOsmJD+OBjr2Ye+TkgMrZ9KfklgvetogCT+DA4JX1X/FUZ1GnJNk0KKixBC5KLn30SWDEMCxiblYXTigJhP4gHAJYa2rr1ORfe23XmvdD/Kbc2y2685theV9pYIRkRiVVHDX4Mm8W2qHZ+jwvZej6+ZY56juI9WSECqfoLs9rsaXguaxLfhkLCj/iXFMRHSX4hgYXmR4CiRJ/1SvCa0CasJWkOYI+n7OOdYWaxsqT4JHG+U7I5QRCRW2T3lOGX7VNHj8DLLG+A9fH6eohuLBO0wRX0K4q+BStDJamvz1qLUtknR+WtcB1DvOqqoDyGEKEWJPOmXZmcOV9xHK6gwIy18E/D6iwa3A8UtdYr7fVddGv5gSMwRJTdOWj/CN5U3YUPFVfCCwQc1vFwFibOgSb3FcxgWT1GPYmCMYVzKPWAyP9IMqgwMS1gi+/wVjh3gkBTHddK+VXEfQvo6DgaJC2F50WTX4CiRJ/3SiIRMTEgeoKjPJTmjkaQzRSiivsvuDW1dfZtX2URC0vc4fdX4unIJdtU9hCb3vg5HOAAGESr4oAqazNu9ZT2OJcM4DdMyHoWAwJNnjepMXJD9H+jVKbLP7RHtIcXkluRPuCekv+AIX2kNzXUNjhJ50m/dUzgXKpmT6MxqHe4cNivCEfVNZk1oq/jEhVjeRPoGj9iMb08th9Vb0s1R1unr4Ml8eD6uB5jnYW7uCuTHXQmBdf651atSUJj0A1yUuxLx2gJF59UIoZXcaRiV6hFCIotm9pGos3rd+LD0EA4318IrSUjTm3Bl/kgMTUjt0Xknp+bj75OuxX273oVX8r+7a5xGj/9MuwH55p5dr79K1hkxIjEdh5trFfU7PzM/MgGRmHC46VnYfXJXh2pdV1rtpzzFoM4OW1yJuqGYkv4wxqX8DE3uIxC5C1ohHsn6USEvdZllHBtiv/Eh9SOkb2NhW34SVFoTFCXyJGrcog9/2/MVVpfshd3XuXzjyQPfYnpGHn4z8SLkGhPwbtEh7Dx1Ck6fDwl6HeYOGoyLBw2GJsha5fNzRiHPlIyXSr7FZ6cOwcfPJBJqJiDLmIBB5lR8V1eCDH08ck1JEfm39mWMMSwdOhEPbf9Udh8VY7hhqPwVQUjf4pMcOGn9QFEfDtbtLssmTQESdePCGF0rrSoBGcapYTlXojYPOcZJqHTI2+0aAJK0BcgyhHYDQAghclEiT6LC6fPili9XY1ut/xG9LdXluOadlRBcanjEziPq7xUVId1kwr0zZmLhqFEBr1WYmIW/T16I+1w2fHbqIN44sQ0nbPUQuYhKRyMqHY3YWFuM54u/wYUZw/GbcVcgXR8fln9nf3FNwWi8cHgbSq2NstovHjweWcb+/z10iS0oblmHE9Yv4RQbITANUnTDUJh4FbIME8Hkbivcx1Q7voaPK60bZ+BgXapeB8Yt7hPfpwnJS1Hp2AW5ZUATU5b1iX8XIeHGAYjh2hAqLGfp36hGnkTFQ9s+DZjEgwOwqOGzsy5JfJtaux2/+nw9nt2xXdY1691W/OfIFyi114OxriODHMBXNUewdOMLqHLIXzP9XGBUa/Hy7MXIlpGcX5QzFL+bPK8Xoooezjn2N63GquPfx7b6p1HnLoLNVwOLtwInbF9gXcU9WFt+O6zeU9EONSJcPmVlVm3OXoEiRT8NefGLwxFSxOWYJmJWxs8h51H/5JTbMCR+buSDIiRGSWBheZHgKJEnva7C1oK1Jw4GbuRQgXlU4DLux/+6aSM2lYlrSj8AACAASURBVAde9cLh8+D/tr4Oizf4Vu5Vzhb8dPubkLjy5ea6wznHtpqT+PPOL3H/5k/w8PbP8VFpkd8blFg1MC4J782/BTcMGQ+DqmutcbYxHr+eMAf/nXUtNELgkqe+bk/jq9ha9xQk7n/jsQb3UXxY/mNYvdW9GFnvkLvMY1dnfp/TDRdiUsaTIdetR8PIxKtwSc6fkaob2u3xRG0e5mQ9hEmpN/dyZISQcxWV1pBe90bJHkiBEnQOwNmaCDKZd+Qv7tyJmXkDz5yCc+yuq8KKot3YUl0OGyyAwSE7xqKWKmypO4EZ6YNl9+nOFxUl+Ovur3GkufM67K8c3olUvQl3jpqKHxRO7TOP4NMMZjw69TLcN34ONlQcRY3TCo2gwpCEVMzKLIBK6P9jAzXO/djZ8KKstg6xAV9X/xFXDHgqwlH1LrM2tP0WBKZBlmkOBsZdjyT9pD7zc9/RQPN5yDNNR63rEE7at8Mj2aARDMg2TkC2YUKf/DcREk6cs/CV1nD6fQqGEnnS63bVVwRu4BbAFP7yflNWipMtLRiQkIAWtws/+eoDfFNZ2n7clOiC0jHiNaXbe5TIrynZi/s2f9L1lsXDINhVaPJ68JeyTXht4178YsZMXFo4DDp13/iVjNfqsWDQmGiHERUHm99W1L7auRcN7hKk6IZEKKLel26YDoM6C05fFQRIUEECO/38TIQKUjcj9lohCRfnfQq1zN1UYxljDBmGUcgwBJ6fQwghkdb/h89IzHH4/JcjAAC8yn8sOYADtTWwez1Y8unqTkk8wKFSS4q2jQeAA82ViuNos7OuEr/e8mnnJN4HqGo10NRqobKrIHgECF4B1Y12/PKj9Zj11AtYV3Qk5GuSyHOJFpywfq2435GWDyMQTfQwpkK2cQYMzA2T4IZe8EIn+KAXfDAJbhiZGyp0Lh0riL+uXyTxhJDAOACJs7C8aLJrcJTIk16XqI3MRkFuUcQTuzZhf0NNt8eVPvF2S76QY3n24BaIHe8cfIC6TgvBI/it+29yOvHTtevw1t4DIV+XRJbNWwUO5XMbWjxy11vvG6ptH6DW9gbUrOsNMueAikkwCh5o0Po7lKqfimFJy6MQKSGE9G+UyJNeNycnSIkBC+0e3KjRYHXx/m6PKR2NB4BETWi7MlbZLdhQ0Xm3S1WjBkxsvZMIVvf/m082oKS+IaRrk8iSeGg3dxLvWxObA2l27UBR/f3A6Ruas2+QO/63XvAiwzAe0zP/3auTWr2SFfXOzai2b0C9cyt8kvz5MYSQnmrdAC4cL9oQKri+UZBL+pXvF4zG3/Z85b/ERicBToCfXnVajiS9Ho0+B6xedzdHGXweFTQ6ZcnU3KxCRe3b7Kk/BanjnYOHtY/Ey/n3iJzj9Z178bv5c0K6Pokckzo9xH5pYY4kekqb/6voqYSKW6ESIvMU7mw2bylOtLyKU7YPIfIzK1SpWRxy465GQfxNMGjCt4ssIaSrttKacJ2LBEYj8qTXxWv1uGv0TP8NNBxcLclO4gHgulGjUWlv8Xvc42wdDZQ7Ms8AXJc/Wfb1Ozr7BkWwK1uBBwDeO1AElzf00h4SGSZNGrIMExX3Gxp/SQSiOUOSHHC6NsHuXAeH62uIkv/fhZ5weMvQ6NqkqI/VcwAW976IxNNRneNbfFt5HU5a3+qUxAOAj1tRalmJTaeuQ5NrT8RjkcMtNsDiPgSL+zC8oiXa4RBC+igakSdRcefIaWhw2/FC0bbuG5h84C0aWclvTlw8fjBpMl4u8r99uugT4HW3jsp3t0382ZYNOg85xqSg1+5Ooq5zSQ7zKh+ZsHs8OGWxYFBKckgxkMgZmfh9VDl3yW6foBmAbKPy5F8On68CzbZnYbOvgcTPJIOM6WE2LkCC+YfQarpf8zwUTa6tIfVrdG5BvG5s2OI4W4v7IHbW3g2Jd/dE7gyv1ILtNT/CjOw3YNbkRywefzjnqHd+g3LLKtQ7N6FtvJFBjUzTfOTFL0Wiflyvx0VIuHW3chWJDPpOk6hgjOHBiXPxzAULMCVtQJfjafFGXD56CLSqwItG5sbH49UF1yLVaESuOdCuowxOqw5ej6o9ifc3On9t3kT8fFToO5NOyxgAk7pDPXCIzwa9Yng2pCLhlW++AAXm2bLaCkyDWZn3g7Hw/6l1uXeionYeLLYXOiXxAMC5C1b7KlTWzofD+b+wXVOUbKH146H1k8MrWrC79v6gSXwbn2RFSdN/e3RNzkXUOr7C0cZ/4XDDX3Gs6RlYPcUB+0jci/1192NXzY9Q79yIjn8YOHyosn+MrVU3oKTpafBQJvUQQs5JNCJPomr+gOGYP2A4jrbU40hzHTyiD2kGM6Zn5EEjqHBsYiNe2bMb7xUdgsN7pmQlKy4ON4wegyVjxyHJ0DoCfln+cDy85X9wif5KUhicFh18Oh+0Bh9U6s6J8pSUfNxQMA0XZRX2aFMXs0aHBYPGYEXx6VFbFQeCrLjZnVSzMeQYSOQwJmB25m/AalQ4bvWfJGsEE+ZmPYJMQ/hHor2+E6iuXwKJt4BzwMnVsHIN3FwFDgYVOIzMizjBjZqG25GVvhZ67fgeX1clmEPrx0LrF0y14xvsrPkVGGyynrS1qbJ/hkLxPuhUyp54cc5x0vomTrS8CJfvVKdjJc3/RpJuEoYm/wxJ+q5PYA7V/x5V9uDLkB5rfgpqwYD8hFsVxUZIrOBgEMNWI0+TXYOhRJ7EhKEJqRiakNrl/cHJyXhkzlzcN3MWjtTXw+H1IlGvR2FaGtRn7SKaoNPjmsEj8WZxoHpcBq9bA69bDUHFcdXgYbhu2BjkGZORZ04J27/nzlHT8FFZEZrcTkhGCYJL2XZUM/LzkGKkRD5WqQQtvpf5O4xIuBpFzWtRavu6fQKoWZ2FwsSrMSz+MhjUoZVnBdNk+Qck3gIvZ6gVjfCetd2ZDwwWroNF1CGOuaFr/hOy09f0+LrJ+vPQOoNE2YhxsuG8Hl/7bDWOTdhWfQ8YfFAxZcvLcnhR59iI3Lir5ffhHEUNj+Ck9U2/bZrcO7G96laMS38cGaaL2t9vdu1Fpe1d2dcqbvwnss1XQ6vwRqMjr1iHRtubaHa8D69YC8Y00GtGIMW8FAmGi8EYffyTyOA8jJNd6eFUUPSbTPoEs1aLSdnBV5u4b/IF2FJdjlJLc5CWDIWJGXh02uUwa8K/SU2uOQGvzFmEW75YgybuBBc4mCT/D9uSiVQnG+sYY8g2TkC2cQJE7oVHtEHFNNAIph490QlGFBtgc7wPH2eoFk2nl2jzh8PKdahwbkeK5yh02p7Vyxs0A5BimIUG5zey+8RrxyJeF95dgEXJhV21D4Gf3lE2FB4p2N+IzsosrwVM4ttweLGv9l5Mz1mNOO1wAMBJ6xuKrsXhRYX1XQxK/IGifkDrDUed9TlUN/8V/KxHgTaxFjbXN9Cq8pCf9gIM2tBW5iKExA6qkSf9SrLeiDcvvQEjkwMvEzg5PQcr5y+KSBLfZlxqFj687BYsGz4RmhTudyOos80dOggXDRscsbgizS368OmJo3hh3w68sG8HPj1xFG6/5U79g4ppYFAnQasyRzSJBwCH60sAHjRIhiBJPNA2em7jWpyy/Ccs189P+BEY5D5hYshP/ElYrttRpX19h0Q8tO+3islfElPiHpxofkF+e3hQ2vIKAIBzCdX29UrDQ7V9neI+AFBreRJVzX/sksR35BHLUVJzLVwe2kmaRIbEhbC8SHA0Ik96xbHmRqwq2oN9ddVw+XxINhhxacEwXDV4BIwabVivlWWKwwdXLcOG8mN4rWgXtlafhMg5NIKAWTn5WDZiAi7MKYCqQ2kO5xw7G8rxZXUxLB4nDGoNJqbkYW7WCGgEZWUxHeWaE/DItHm4b+KF+PfmzXj1uz2d15g/y9yhg/CPqy+DEOFkMBKcXi+e3rMVq4r2ocHZeQOeFIMRNxaOxY/HT4NB03sbA/VHktQELxfg5Gq0lrgE+1lpPV7r3ImCMFw/QT8Rhal/Q1H9r4KsJ88wLPm3SDVeGIardlZu/aD961CfvCcqWEWnxv45PJKyTdqqbZ9gePKvwKCSPRG3I4+ofFM4h2cfqlv+LqutxK0oa/gJhmV+FvGbT0JI5FAiTyKqxe3CL776BBvKSroc+/rkCTy65SvcN/UCLB05HkctNVhTth0bqg6h2eOAVlBjdGI2FuVPxZzMEdAI8n9cNYIKl+YPw6X5wyBxDrfog16l7vYD66vqYjxx8H84aqnt9P6KY9sQr9FjXtZI3FU4G+nGOOXfgNPMGh0euGA2bhw9Dq/v2ot39h2E1e1pPz6rYCBunDgOc4YO6pNJfIvbhZvWvY29tdXdHm9wOvDkri34pqIUr122EAm63tkgqD9iTA+b1HYzJPdnhcMu1sLhLYNRM1D2tSTJCklqAGNaCEIGGGu9qc00XwG9OhMnmv+DJtd3Xfol6CYjP/GHSDHMkn0tJRzeyvavOZiiia5AaxKfoJNfVtLk2qEkPACto/It7n1I1k9V3BcABKZ8gKPe+oqi9i7vYdjdW2HWT1d8LUL84WAQwzRJlSa7BkeJPImYFrcLiz98A4cb6/22sXrceGjT53ivbDeO+k50OuYUPdjeUIrtDaUYYEzGk1OXYFCc8h0yBcZgUHc/Cvx26S78dveHfkf1LF4X3i7fhTXH9+DizFH4QeE0jE/NURxDm/zkJDx40WzcO3smqq02eEQRqUYjkoyG4J1jFOccP9nwod8kvqO9tdX4yYYP8dplC2kUMEQ67UR4FVdFtn6vnb6TQRN5ziW4XRvgsL8Gj/tLtI15C0IaDKYlMJqWQqXKRqJ+MiZkvgy79zianN/BJ1mhEkxI0k+FWTsihH+Zkn9Ox58dBgkCVJBkJ/QFCleE8UmO4I26IUoOCEwPozoPDl+5or5m7TCF17Kj2fFB8IZnabS9CbN+OhyeEtg8hyBxDzSqZCTqZ/TajrxK+CQ3BKaGwEJ/UkoiL1yTXUlwlMiTiPnttxsCJvEd7TxZD2OyCmpd94/qTzoacet3L2LlzDswwBSeTZJ2N5zE73Z/FPTRPOeAoJawvuogPj15BH+cegmuHzKhR9fWqdUYmJTYo3PEim1VFdhYUSa7/caKMmyvrsTUrNwIRtV/6bSjIAhJgBjK2uyBf9olyYGWph/C7drQzbE62K3/hN32LBKT/gu9oXWvBZNmEEyaQSHEEjqjOhtOX9WZ2MAAzqBiwQtthiT+EFmmixVdTy2E9jROLcSDMYbc+EUobnxMUd8BcYsVtfeKVeAhlPDYPXuwt+oGWNzbO72vFhKQYV6I3IQ7oFWFb0WvUNS5jmNf8wcotnwJj2QHACRr8zA68QqMTJgHnSoyy5sS0hfQTAISEdV2Kz46dlhRH48jcO10k8eBh/e+35OwOnnx6LeQZFTYto3wqdQSJEh4YNsnWH+y/04S45zD5fPCJ8nbkGrFIeVb3r92cLfiPuQMs25KSP30Kv8rP3Euornxjm6T+M4NnWhu/AHc7k0hxRAOeXHXnPVO66i8yAW/y9WpmBGjUx7GsCTlk2/TjMpLhFTMhER969r9OeYFUCtYS9+kGYRUw0yFVwxtAzmn93iXJB4AfFILKi0vYs+pBXB6S0M6d09JXMTXNU9jVekdOND8UXsSDwCNnnJ8U/s0Xj62FCft9PcklnCEb7IrrT4ZHCXyJCLWHNkPUdECsBw+lxqSGPhx3PaGEzhmrQ3YRo4qRwu+rAq8E+PZGAMEofXf9JfdXwSctNoXHWisxv1b1mHsmidQuPoxDH3jr7jkoxfw6pEdsHhcfvspGY3vSR9yRl7iz09/Jf9nMF47Diat/9WQXM734XF/IfNsPliafgXOo7P7cI5pHrTC2Wv0tybzPqjg4wIkztpfPq7GjOzVyItfGNL1Ug0zYVAre4KUE3c11IIJAKBVJWJ8xr8gIHjdu0ZIwoSMpxTvBqxRZQCyVxM6QwpSg+wWK3Gg5lb4REvAduHGOcdXNU9hT1Pg9ffdkg3vVzyASsf+XoqMkNhCiTyJiCMyS2rOYAAYJF/wH8m1J+WNvri8XnjE7kt19jVVyhqNPxtTtfYpszVhU9WJIK37Bp8k4cGtn+LKT17G6mN7YfOdmYR7pKUOD+/4HLPffwbbak9229/qUf443xZCH3KGWTsMibqpULL0Ym78koDHHfZXFcUgiqXwuOWvJx9OKkGHSel/8rMMJgOHABGq9teolF8hThv6mj2MqTAk6W7Z7TVCAvLjO9fhpxjOw+SsF2FU+5+jkKAbi2nZq2DS5CuOUSUkIMEwX3E/r4wl/ly+k6iyBV9DP5wqHHuxvzn4TrgAIHIvPq/6W9RuLMnZGKQwvUJdXvZcQjXyJCL8JdDhUGFv8nvseGMjVu3dh7WHDqHJ1TqKnBsfj8Vjx+DqwkLotCoYVBo4Rf9rLAfScfOZ9RVHcEF279YGhxvnHA9s/QRvHQ+0Gy7Q5HHi5i/exKqLbsSEsyb7mjVaWBQm5qYwLzl6LhqR+gfsqFoMn9QStG2acR4yTFf4PS76KuD1dC2vCMbpeBc6/WzF/cIh3Xgepmc+ie21v4JP6n6+AIMao1PuxaCE63t8vWzzFXCLtUFr3dVCPCZmPAODpuuk+CT9JMzM/RgNzs04ZVsLl68aYCqYNAORG7cQCT3cOCs17ma0OOWvPy9xwCdzPK/a+gZy429vX7ko0vY1KyujbPFWocy+A/nm0FYJIqSvokSeRES60RRSPyaEVq7COcdTW7bgn99t7nKswmLB45u+xeObNkFK8AF6CblxoU2O4h1m4je5Q1vJIpZ8depY0CS+jUv04ZebP8bnVyzvtOLMedl5WF96VNF1Z+TkKWpPujJqCjAxcwX2VN8Bj+R/xaAE3USMTH0sYAImSjUhxSAFuG5vSDfOwLy89aiwfYwyy7uwecvAIcGgSseAuKswMO770KtTw3a9goTbYNYMxYmWF9Hk2tbpmMB0yDJdhoLEO2AKsDIQYwJSjecj1Xh+2OJqY9KdhxTzUjTYVgZtyzng5BrIHfF0+U7C4T0Gk8LVdELhFm04Zv1Wcb9DLesDJvKc+8AhQmCR2wiQtP5siWFataafVbBGBCXyJCKuGlKI14v2KuojqEUI6uCPRnNNZ9fGAk9u3oJ/be6axHfGwFrU4PChAlbojPLXnm5b1k6SznQwqORtbMQ5x/aGUnx26iCaPA7oBDVGJmbhytzxSNBGd9nJFcW7FLU/ZmnA5poyzMjMb39v2ajxihP5ZSPHK2pPuqcW4qASdAHnOba4d+FY098xNPlBv0t+srM+ChgYDEwNHVODgYGDw8tFOLkPYqeLRX9zL41gQkH8IhTEL+qV66UZZyHNOAs2zzG0uPdC5C5ohASkGM6HVhXdlagYY8hJ+iMYNKi3vRygpQoOLsjYGbgzOU9/wsHmqwcPYfKu1dv1htQrNqPW/jZqrW/C6TsOAFALiUg1XYVM8xIYtUN7HC/prG2ya7jORQKjRJ5ExNTMXAxLSkVxk5xa+dbdKbVGr6zE+poBnZd+PFJfLyOJb8XAAIsaXCdB9AlQa+R9WLQm8QDvkMjLWU/+u9oS/OXAJzhh6/x9+LBiL/55aAMWDJyIX4ycB53Mm4JwqnfZ8dWpY4r7vXN8f6dEfkZOHqZk5mB7TQWgltBe1sgB+Bggda5znJKZQyPyYSBKTuytWQ6nL/jE4QrrCmhUyShI/HG3x1XqgQC0ADwwMS2MTNMl6dcwFYzQws19sEgucABqTeRHZ2OVWTsY5gCTh6OFMRVykv+AZPNi1NtWoNn+PiTeWnqkUw9GinkJah2bIbrkzW/w8tbVgBg4mIzJuuHAQqyLPnuCcJPzKxTX3QWR2zu975OaUW19DdXW15ATfyfyEn+peHIxIbGCEnkSEYwxPDLzIiz9eA28QZcxZFBpRGiMwevWp6QUYHBceqf3Vu1RNvLPOAN3CRAFFVRqSdbNA+eA6FWhLSE1qjW4pmB0wD6fVh7A/bve9rt6j1vy4Y0T21BiqcV/py/t9WS+2mENabSj0t559QqLx4WEOA1g6WZehJq3jhZ7BEASMCI5Dc/Ou5o2gwqDatta2L1dd0z2p6zlOeTGLYVGFd/lmCAkQq+/HGr3OhgFDbifn1nOOXRMjUTBgGbJCaPxxpDjJ5Fl0I7CgOS/IDfpz+DcBcbUYKz1b4ybMzQGSOQlDlgkIxpFE1z8TPJeWfFbFCZciZGJV8GsSffbv6fM6jSomAYiVzaXKUFzZnnVZucmHK69Axy+gH0qLc+CQ0R+0gMhxUq6w8K4IRR9VgRDt6AkYqZlDcCz866BQR34fjHZpIUh2RE0oU7SGvHwuKs7vSdKEt4rKlIcG3OqwDmDx6X2W4PX9j7ngM+rgiSd+XVZMnQi4jT+6yxLLLV4YNe7spbg3N5Qir8d+FRR/OEQ6NvNOcCl06+z/gkC6zhPwInr1r+ODZUBEkoBgE7C3IICrLnqeiQbjD2KGwBsHg+ONNThYH0t6hz24B16mc1zDHWOr1Dn+BI2T2vZEecc1Y69+KLqYaw6vgCvlVyKN48vwrc1T6DRfVzR+TnnqLCuUtRH4k5U29f6PW7SjmpP4v2W4Jx+X8NUiFfnQ62hsoRYxxiDIBjak3gAyDAv8Du67uMCSr1pOOVLOp3En/kD4BKbsbtxBdaU3owKu/LJ0XJpVUYMi5utuN/oxMsAABL3oqThl0GT+DanLC/A6la+HwYhsYBG5BXyeDxYsGABjh49itWrV2P8eKr1DWRO3mBsWHQ7Vh7ag9WH96HR5Ww/NjE9C2Yzw25b8FHFAcZkPDl1SZddXa1uN+wej59eAZxer55LAtxODdQaEVoNIJ1VlymKDKJPAO+QxH8vewjuHTc74OlXntgCL5e/cs97J3fj/0bMQbIutEnCocgxJUDNBPg6LNnGOQAvA3wC0GFEhQsc0EiAimNg3Jk5Cj//9iMcbZFRPsWAb+tPwOJ1IV4X+kSzg/W1eHXfLnxQchgu35kP6VtCPmP4cC6h2r4OJy0r0eLu/JTIqBmJGp8WjZ6KTu97JBuKWt5DUct7GBx3MWZl3Ae1EPz74xarYPcq2wcBAOodX2FA/E3dHlN5Wkdo5T4t0XILuNQCJiQojuNcwbkEi+sbtDg/g09qgsD0MGknItl0DVQh7hYbDhpVMrLibsQp6yud3pc4Q7k3pdMofHe3/F7JgU8rH8CVA/6FDMPIiMQ4NulqFFk+l90+RVeAbEPrqj+Njs/hEZVN4K62rkScjj7Pw4Ej+P4ESs5FAqNEXqEnnngCR48qm9h3rssxx+O+qRfgZ5POxymbBS7RhzitFg/v+wDf1p5O4rkASeQQGO80Ms8BzMkYgcenLIZG6LrqRsglGh27cQafR43LsgsxKDERq4/vQa3Ddnpi65mGJrUWS4ZOxC/GXdhtLG1sXhc+rpC3EkwbryTivfJduH2o8h0kQ5WoM2D+gOH4uLz1iQYXGeAS0PWDm7fWubtVgMBxae5wAMDR5np8WSm/xt4l+rCyeDfunzg7pHhf2bcLv9/0RdA/7C6fD/ogT4HCTeJeHKx7ANX2j7o5xlDiOAVvkD+3x6yfwyNZcXH2nyGwwG29Umib8/ibrMi9RwGvsonPDB7A+T5g6v7G4FzX7PgUFc1/hMdX3un9Rvs7qGz+E9LMy5Cd+EswpkGLuxhl1ndg9RwHhwi9Kg255suQYZwZseUeC5Lvg8tXhkbnl2diO6uUJhCRe/Bt7T+xYOBzEYkv0zAC01Jvwtb614K21QomXJL9QPvnQZ098CZS3am3f4RByX+EStAr7ku6Cl9pDQmGEnkFnn32Wbz8cqCVAEggWpUK+Qmto7lPHNyAb2vPTgJP19WdlaltqCrG19XFuCi7sMs543Q6JBsMaHQ6uxwLSNU1HdSrNPjp6Nm4e9SF2FJbhvUnj6DR7YRepca4lGxcnT8K5gDlNG1KrHVwhbBO/YHmSsV9euqm4ZPwcXkRuAg/STw6vycxPLZ1E6ZcNQCvH1W+Lfqakr34+bhZ0KqUJSdrivbj4U3ydh39xf/W4al5V/ZqHf7hhke7TeIBoEkyBk3i25y0b8Gh5rUYnRR4B1IVC608yW8/UX6tfUfcd4wqWLtRb3sd5Y2/9ntc4nbUWJ+B3XMQdT4d6t1db6Iq7ethUGdhfOpvkG6cEfYYBaZBYfrTKG/+D6qsK+EVm9EkKnsiWOc6glpnEdINXf82h8O0lGVQMx02170ECd0/4YzXZOKKnN8jVXdmwy+3T/nfUg4PvFI9VIKyHXwJiTZK5GXweDx49NFH8eabvbuzXX/l8Hmw+oSy+spXj23uNpEXGMPC0aPw3PYdis7HDV0/FAriUwC0jvKfl5GP8zLyFZ2zjTvEzaZcorx6znCamj4APx51Hv6zYxvkTiraV1+DFQd340CD8rXHG91OVDksncpzgrF5PPj9pi+DNzzt42PFWHyyDBfk5SuOLxQ2zzFUWld3e0ziDHZJ2QjfoeZ3MCpxQcBVNAzqHOhUGXArLB9I1E/q/oCCMrDOQvtZ78/s7l0ob3xQVlubeyO8kh5A9zdYTl8VtlT/BFMyHkOWaU4Yo2wlMA3yk+7BgIQforj5JRTVdf9zHMgx6xcRS+QZY5icshiFCRfhYPMnOGL5Eg5fIwSmQYouH2MSr8CguBlQdXmCFeKqN3RbGhacs/AtP0kj+0HRZNcg9u3bhxtuuKE9iVcpHEkkXa2vPAirT9lOoDsbylFiqe322I3jxkEtyP9R5gIHdJ1r4dWCgGsHBV6FRq5EbWijpYlRWlN+fvbw00tEyq9GfL1oL1y+0JI4t8IblrXFh2D3KpsHseJg701cq7D6v8G3SzpwhcmBxVuBamfg0izGVMiOW6zovIDgv48qxBVIhIzQ+vVjNZbnEHBh/7OYmavTjtFn45Cwo/bXcPoit/mWStBDrwnt75/D1xDmaEx8mgAAIABJREFUaLoyqVMwNXUplg16EXcOew/Lh67Bgry/YWj8Bd0k8YBek6/4GgIzQaMK3+ZhhPQWSuQDeOyxx7Bo0SIcOHAAADB37lzcfPPNUY6q7zvcEtoH0hFL96OPAxIS8PBceaNVHBw8wdtlwObyvEKkGULb7fVsQ+PTkWuUP+LcZm5mZEa1gvnw+OHTX8lPOMsszdAEqeP2J0mn7Ebnw5LDwRud5X+lxxQn/6GqtW/we8yL0G78Ld7gpQE5cddDLcjfgCjTfA306qzuD2omAUJ298cCYIYrFffpz7xiLZqd6xX1ERhgZIEHNiTuRqnl7Z6EJiOO0Ja/DbVfJGWYlW8Olma6hnZ8DSOJs7C8SHCUyAewd+9ecM6RmJiIP/7xj3j66adhNPZ86bxznZLVXDryBBjJvWHsWPx1/ryAkxy5wMGTvIC28+jX4PgUPDzl4pBi6o7ABCzOn6KoT7o+HrMzh4ctBiVqQ1y+cXSS8tHYqem5SDMoq8OtdzgUX0fiXPm8iRB5paawn1POrpZaVQrGpT8DFQt+A5qon4bhyb/ze5wxFZjxekUxQnsemHqQsj59GOciGh1foLjubhyoXowD1TfiWMNvYHMfaG/j8BwA/NRyB6JlwfuUWd8DD7kEKrhkXQFCKUlJ1sXez0Ci/gLo1QMV9GDIjFsasXjONW2r1oTjRavWBEeJfADx8fFYvnw5PvvsM1x33XXRDqffSNaGtsRiij5wwrJw9Gh8d+cdeGj2bIxKT0eywQCDVg1oJEjxXvBUT5ckfnpGHlbPW4pEXXjLWhYOnIRB5jTZ7e8dNQ/qACvhRJKSsqSOZmTlB1xLvztLh01UfB1NiOVsul4qg1Mx/zXw6hCSOgAwqzNltUvQT8CkrDeRrJ/Z/fVZHPLif4DxGS8EX43DeBOgHiUvQBYHFveQvLb9QItrM3admoPDdT9AveMjWNzbYXFvQY3tdeyrvgr7qxfB5auAxF0hnV9OuuIWG+ASI1fGEqfJxACTsgEIFdNgWPz8CEUUOsYEDEv9NwQm7+/6wMT7YdJGZyCFkJ6iya4BPPnkkxBCTHKIf5fkjMLTR75W1CdJa8S01Pyg7RL0etw6aSJunXQmYax2WPHm0T34rKIYzW4n9CoNJqblYOmwiRiXkhX21U1ESYJP4vjrhIX45a41KLX7//BlAH495jJcmjMmrDEoMTwptLrQ0SnpeGDS9/DrLfI2s5qWMQCXDlT+YVmYkobDDXWK+qQajEgJw8ZTcsTrxqLBubHbYybBjSap7cZV3s+ZSZ2ObOME2dc3a4difOaLcHhLUWv/BB6xHoxpYNIMRYbpUqgEed8HJhiB5BfBm34IeAPMMRBSwJKeBTtHNoNqcn6Jw7V3BtxcyOregf1V12JQiLuDyl1zW+KRLRcbk7QIJ+3bZLcfGj8PBrX88q7eZNaNwaiMVThS9yN4xO7LORk0yE96AFnxVDIbblQW03sokQ/gXE3iW9wuvFNyADtrK+ESfUjQ6nFR3hBcnDck4Prpcg2JT8fU1Hxsqy+V3WfBwAnQqUKrxcw0xuGecbNwz7jIrtF+3NKA14/uxtvH9sHiba15jdPoMCKpALW+ejR6re1tVYxhTmYhbho8A+OTB0Q0rmAWDBuFv2/fBK8kf/R4Vm4+BsQn4ob48bB43PjzrsCrykxJz8VzF14b0s/PklHj8F7xIUV9bhg5Fqpe+v3NjbvebyKvYhxG5oaDy1+5ZkTC1UHXke+OUZOP/MQfKe7XEROSgeTXAddn4I5VgLfD6lKqPDDjDYDhWjAFtfl9mUesw5G6u2TtEOqV6nDS8hz+n737jo+qyh///7p3+kxm0hOSQAg9QOgICihIEWwoUkREdNW1ra5fy1p217If3XX34/5WP7a1V5qiFBXFAhYUpSm9Q0INCekzKdPu/f2BIDFl5k5mJu08Hw8eD8w9Z+4bJJP3nPs+76OXk/ApQRySdobqoHq3Sxh12vfeaNHJdhaDE6/lp+K3Ao5NNvdiRMrtEY2nqeymAQzO+JqSqs847lxItXcvKj6MuhSSbJeREjMdo9jgKrRyIpEXTvMqfv614Rvm7tpUpxXi4v3bSbHYuG/IaKb1aHp3l/tyJjJ79etB9VvvZIvnhh4jm3zPSHpt5zr+/tPKOg/InV43GwrzkYA5vUYwumNnTLKeno5UkszNd7LjmZIsNi7v3ptFe7YFHvyL63N+bWN4c9/hDE3O4I1dG1lxaHetk2J7x6dwTc/BTO2Wg0kX2tvNkA7pDEjpwObC4DZJm3V6rurTP6R7hSLZOhqboRuV3voPx4rXVeL2GfAHsfE1xdyXfvFau9GElyQZwHIxkuViVKUClHKQLCdX4qPYm78lKHAuRFGD36NR5d1JB9ullFUtCXqOV5Vxq4G/NzpYz8MgR/7k56GJv8Osc7C+6HW8Sn37ZyS62kczusP9GIJ82tOcZMlIku1SkmxiY3a0qIRvo6rWrl/tkUjkBQB8isItq5ay8nDDJ3UWVldy73efUFJTxU39hjXpfn3i0njx7FncvnYhrkZaUWbFJPLyObNDbukYDW/sWs/jP61sdIwKvLX7JzJj4rk+u2l/d5Hw8Iix7CguZHtx/S0+z3TrwOGcn1l7g9uQlI4MSelIcU0VuRUleBQ/KRYb3RxNT/4kSeL5iZcydfECCipdAcc/PeFi0u2OJt1TC0nSMTD1v2zIn43bX/fvTy+ppOrLOeFzNHowVLp1COPSHkMvt5zOGZLsADl6f5fRpqo+PN6t+P0lSJIFo7EPul+eNqiqSoFL+9khHqUGo64jHv+RIO4PZYqVYMqushzR2aclSRL94qeRHXsReyu+JNe5GrdSjl4yk2LuTe+4ycQaM6ISiyAIgYlEXgDg6Z+/bzSJP9M/NnxN38RURqZr6QpQ17DkLnw47jYW5K7ng7yfKPH8uvKVFZPIVV3O4orMQdg0bqiMpoIqJ//4KbgTRwH+8dMqLszMJs3aspIju9HE/Euu5N6vP+WLg/Wf8mnR6/l/Q0ZyU/+GN8Qlmq0kmsP/oaujPZbFV8ziri8/YV1+4wnSpK7Rr922GjoxLH0hO4v+RlH1t/y2J79BUuhr74PNPJH9rtUUVG9FxY8sGehkO4c+sVNItw5u9BAooS5VVShy76DaV4ws6YkzdiHGELiNpl8pxel6A2flXPz+/NNflzBhs16OI+YmJF0anjOuBaval0vvlHfYV3hNgGRepkSxUBNEWU2adSwplug+lTTIVvrETaZP3OSo3ldoA9Qw1siLtjUBiUReoMrr4a2ddY8Ib8wr29Y3OZEH6GCJ5a4+4/lD9hgOuoqp8nmJNZrpbGsdj/EX7ttUq5QkEL+qsnDfJu7qf14EowpNrMnMKxOnsK+smPk7NvNz4TGqfT7izRYmZvXgip59cRib70NVht3Be1Nmsu1EAQt2bGFfaTE+RSHVFgOF2v79RoJZn8agDi9S5T3EMdcSqr1HABWzPoP0mMuxGU8eId87fgaqquBXvegkY6v4d97S+JQadpd/wN7yJTi9tZPldOvZZMddSYbtnHrnen25FJy4Cp//YJ1rKm5cVe/iqlpMXOxjIcWmqG7Mhm5kd/iYQucbFLnm41N+3awtYSDeegkpjhvJr97CtuJ/01i20sE6hiEp/xD/ToRWQyV8ibzI4wMTibzAx7m7cHq1nbT6zdEDHHaW08keG5YYjLKeHo7Wd0rkktzg68pPWXxgW4tM5E/pHpfIwyPCfxx8uOQkp/L30bX7/j/6Q/Mn8qdYDZl0j7+z0TGSJKMXh8+EpMZXyspjd1Hirv+gsGNVP3Ks6kdy4q9lYOIttRJgv7+EghMz8fkPBbiLl7Lyv2DCiLZ3RjDqTrad1esSSI+7h7TYP1Lp2YTPX4osmbEac9DrEgDoZuxHonkIueULOFK5AkX99W5J5mF0ccwgzTZWPKkRBKFBIpEX2FKs/aRVFdheXBC2RL61yq9yBh70G8ernKiqKlbYBEEjv+rlq/x7G0ziz7St9C1MOgd94q8+/bUK1ytBJPGn70a83sBxnwctByUlWi+u9d+SZCDG1HA5Wpwpm/5JD9Itbg41viIMshWzPhWLPgW/4qHaX4yEDpMuFllqnrMmBEGrYFuqCk0nEnkBtz+0Q2vcjZy02l7IISTjsiSJJF4QQnDQ+SVFNduDHr+5+FW6Oy7HqLOhqh6clfM03U9WKzBixBNExyEAWbKQEjM1qLGqqlJSs5G8ivfIr/wC9ZfDwwxyHEmWUbj8fg5XrT3dO94kx9Ij9hJ6xk7BHsQ+AEEQ2geRyAskhHiqabw5vKehtkZdHYnsKC3QPEcInqqq7K4o5GhlGRISmTHxdHcEf2qu0HbsLv9A03ifWk2u81N6xU3D7dmAomg7WAwg3tiZAk/gDjQAneMfQB9Elx9F9bG16DEOOeu2qfQqZeRXfoyqgh4THk6en+FWytlWOo8dpe9yTuoDdHdcpO0Pcur1/SUUVX5ApWc7qurBoEsiwXoRdtNwscAghIVoPxldIpEXuDCrFy9tC/40PziZ/A9Lbd6DjFqCmd0H8PD6zzXPEQLzqwpLDm5h7v717CirXf41MCGD2d3O4pJOOSE9FRFan2pfMUU12vekHHJ9Ta+4afj9JSHd127sg08/gOKq5Y2O6xx3P2n2awK+nqqqDSbxZ5IksOFGVaVabUsVfHxf8Dg6yUAX+4RGXqE2v1LNodLHOeF6H5XaJ8QWON/BYuhJ5/iHiY1ydxyhbRInu0aP2EEjMCCpA/0StW00ndGzP2a9+Bw4pUsODg3tMe0GE1d07RfBiNoGt9/HH354jz9v/KhOEg+wqeQo965fyn3rl+JTgu8aJITGp7gpdu+jsHo7FZ5jqGr0e0m4/RWhzVNOzpOk4E/XPZMs2eiZ9H9kJ79EnLn26dASRpJtU+nfYSkZsTcH9XrFNRsDJvGnX18Cm1xDfb07fih8Eq9SHdTr+JUqdhVeQ6Frfp0k/pRq7x52FV5HSdWKoF5TEISWQWRiApIk8ddhY7n6s3eDSooyYhzc2HdoFCJr+WIMJp4993Ju+GpRwDaUeknm2VGXYW/BffFbAlVVeWDDh6zK3xtw7IeHt2HTm8TD1wgp9xxhR9li9lZ8gueMUz4TTN3pEzuF7o6JUTvAKtT7nOoOZDTmADpA254gk3EAkiSTYJ1AgnUCHv8JPL4CJEmHSZ8RVCnNmQ5WvKtpvCyBUfLhUQ21vu5VXOQ6v6BnbOA+73klD+Nybwzibj72Ff0/+qV9gsXQNfBwQaiHGsY+8s2wZtDqiBV5AYDhHTrx/JjLMOka39TVKSaWdy6YQZIl8keFtxbnpXXljfNnNHoQUqLZyuvnz2B0ercoRtY6bSk9xvIjwW9oXJAbTIIiaJXn/JbFB69le9miWkk8QIl7H98VPsnHh/9AlS+0khWtrPoULLokzfOSzH0B0Os6YLVM1DRXkmKwWa+o9TWjLpkYUw42Y2/NSbxPqSa/8ktNcwBMUv2NBQ44A6+ee3zHKapcGvS9VNVNgfPtoMcLQkuydOlSevXqFdSvxYsXN3e4YSFW5DW64447uOOOO5o7jIiY2LkHyydfxxs7NrJk/3aqfN7T19Ksdq7OHsjs7IHEhbg5ti0bldaF7y//A58e2sX7B7Zy0FkKQGZMHNO79efCzGxMOvHtFoy5+9c3dwjt3rGqjazMf/h0J5WGFLl38+HBKxniGECCdSzx1gnIkqHROaGSJT09Yi9jS8lrmub1jP01EXfE3ExV9acEe8yM3TYHWY7RdL/GePylAf9O6yM1EG+VL/Dm3ULXu2h9ClHk+oBOcfehk8N/SrPQPjRXjfyuXYFb07Y1IrMQaukel8jfR1zAA0NHs7v0BFU+L3EmM30SUtHL4gFOY0w6PZd3yeHyLjnNHUqr9uWx3c0dQrumqiprCp8KOuF0+WvYXfEZyVUfYNCl0DHuHlJiZkQkth6xU9hZ9i5exRXU+E620TiMmaf/22waRkLcPygpezDgXIt5LPGxD4Qca32kMPeBl4Joi1np2aL5df2qixpfLjZj31DCEto9CTVsiby219m5cycAvXv35oknnmh0bFpaWshRtSQikRfqZTeaGJrasbnDENoZr+Kn0lf/ZjwhOvKrf6bMc1DTnBLFRpLqwusvJLf4fry+AjLiwv/k0qpPYnTaP/nq2D341cbPXE0w9WRE6kN1vu6IuQ6dnEBJ+WP4/XXbSkqSBbttDvGxf0EK89MFky4Bg+zAq2jbuOtX619EiTV2DjhXCXJDbN15NSHNE4TmdGpFfsCAAfTu3buZo4kOkcgLgtBi6CUZvSQH3DgsRM4B5yrNczyqgRrVgEU6WY53pPw/WI19iLeOC3d4pFmHckHH/7Ku8N8Uu3fUuS5joItjImcl34VBrn8vj806GavlYqprVlJVvQK/UoIsWTEZB2OzTUcnR+bEalky0Ml+OQfKtdWgu9X6f1T3jL0s4Fy9Ll7TvX6dFxfSPEFQCd/Jrlr2uubn51NWVgbQbpJ4EIm80AJV+7x8lLuDzcXHqfZ5STRbmZjZkyHJGa36wJLjVU7m7fmZxQe2UVjtQifJdItNZGb3AUzp2pcY0c0GSZLon5DOT8XBHcAjhF+1vzSkeT5VB/y6rya/4uWIJPIASeY+XJT5OsU1O8l1fk61rxhZ0hNn6kY3+0WY9YGTV0nSYbVcgNVyQURibEiW40pyy+cFXbrkU2V89ZTQOAydSLcODzg/3jqRkqpPNMVoMfTArBdda4TWZceOXz/Y9+nTpxkjiS6RyLcBqqqyufQQ+1wF+BSFZJOdESk9sOiMzR2aJj5F4enN3/H27o1UeGo/Nn9lxzqy45P565BxjErPap4Am+AfG1fyyo71tVYXvChsLyngoXWf8+9N3/LcuZdxbnqXZouxpbiq6xCRyDcjXYjlJJJUe+3M6V5HlWcPVmPPcIRVr0RzbxLNrWvlzWboxIDkR9l0om7Zz28pKrgUM7+tEzbINkan/R05iJr7BOtEDsqJ+JTioGNMsc9u1YsmQvNrjs2up+rjdTodPXtG7n2npRGJfCumqipLDm9gft4PHHAV1rpm15u5tONgbug+mnhjy28V6VMUbv1mCV8cbrh3+K7SE1y78l2eHjWZS7u0jh/eqqoye9V8vj92uNFx5Z4afvfVIt4eO4MRaVnRCa6FujCjD8/s+JbDlcGtDPdypAD5kQ2qHUk0deeAc6WGGScTeJPkrXOl0rM1ool8a9XJfhmyZGJr0eMN1sv7VBmXYkb5TZdohyGTMWl/J94UXCtbWTKRGf8gB4rvDWq81ZBNsm1aUGMFoSU5VR/ftWtXcnNzmTdvHj/++CPHjx/HZrPRq1cvJk+ezJQpU9AFaLXdmohEvpXyqwqPblnM8qOb6r3u9NUwP28N3xbu4sVhvyPdGlqdZLQ8+fM3jSbxp/hVlbu//5jucYn0jk+JQmRN89DGT/j+2CGC2XnvUxTu/P4jvp9yG8Y29CajlVGn56URVzL727cpcVc1OjbN4uDFEVfyyvdPRym6tq+H4yI2Fr2GQv29y+uScMhVGKS6+xoUtXk3TFZ78yhwzae8Zi2KUoVOthNvGU1qzEyMem2nWYdbRswkUq2jOeZawVHXcqr9hciSgRhDFzrGTKbCW06u6zMqfYXI6HAYO9Mz9jIyrMORJG0dxJJjpuJTyjhU+nij4yyGXvRKeVO0nRSaRFUJW9caLQdCnVqRP3r0KFOmTKl1AnVZWRlr165l7dq1LFq0iBdeeIHExMSwxNjcRCLfSj23+4sGk/gzHakq4fb1bzNv1K0tttTG6XHzzu6fgh7vVfy8vmM9T468OIJRNd2e8kLm79kEQbSIO+VEdSUrDu1mcpfI1PepqsqG4jw+PrqZguoKdJJEl5hkrsgcQld7ckTuGYrujmTeG/M7Hv35U74rPFDnugSMTevJI4MupINF26E8QuOs+gS6Oy5gT0XwddVJuvrbQerl5tkw6Veq2V/8IEVVH9a55vJs4kj583SwzyEr/gEkqfl+DOplC5mOKWQ6ptS51gHoGXdp2O6V5riBGGN/8p2vU1r1BWf2ljfpO5ESM4tU+2x09fTNV1Uf1b5D+JUq9LIds76T5g8TQnsihbG0JrjXcTqdHDlysiSzqqqK5ORkrr76agYOHIjJZGLnzp2888475ObmsmnTJm688UYWLlyIydT696aJRL4VKnG7mJf7fdDj8ypP8MnRzUzNPCuCUYVu6YHah08F48O8nfxl6NgWfTjV3P0bUPzaf9gt2r8lIon85pJDPLp5GftdtQ+RWV24l7cPrOGcpG78beDldLBEpmOHVpkxCbx+7tXkOotZcnAzx6rKQZLItMVzRecBdLSJrhqRck7KnZS6cznh3hlwbJquDJtct2WoLFmJtZwbifAapahudhZeT4V7bYNjVHzkO1/H6z9Bj6Sn2k1Sajefhd18Fh5fIdXePSiqG4MuEZuxX7097j3+Io45F5DvfBeP/9fyTYs+izT7TNLs09HL9mj+EQShXqdW4wFycnJ45ZVXSEhIOP21wYMHM3XqVG6//XZWr17Njh07ePnll9vEAZ8ikW+Flh7eqLk936KDa7mi09AWuYHpp6Kjmue4/T52lBS02HpyVVX5MG8rWg+zADjkKgt7PGuLDnD72rm4lYbLJX4o2s81373C2yNvJM3acpLkLvZE7s4Z29xhtCsG2cqFnZ7m+4InOeBchUrd9xsdftL05cTr6i9/SrJdjl6O/tOSw2VPN5rEn6mo6iMcruF0sM+KcFQti1GfglHfeGlihXsz2wpuwqvU3atS7cvjQOk/OeacR7/U17AasiIUqdBahe9AqOAMHjyYzz77jMOHD5OdnV0riT/FbDbz73//m3HjxuFyuZg7dy633XZbq6+Xbx/LEG3MmqLAteS/tcd5nGJPcKchRlu1xtX40/P8wdbwRl+lz0Olv2UcbFTqruSu9QsaTeJPKaip4O4NC2vVFgrtk1G2cn7aI8zo8i4DE+bQwZyNTfLgkKvpqC8m25jfYBKvlxNJj701yhGDX6nhuGuhpjn5zjfFv/ffqPLsZ8vx6+tN4s9U4zvMluPX4fGdaHScIESaXq8nKyuLc889l+TkhstE4+LiuOCCky1ny8rKarWsbK1EIt8KOb2hbSALdV6kxRlDK4+JM5rDHEn46E4/qteeIHQKc8nIksM/4fI1fgrmmbaXH2NjibaTPYW2y27owNCk33NJ5itc2PGfdDFWEa+rRm5gwc0gJ5Od+hYmffRPhi6pWoFfKdc0p9q7D6d7Q4Qiap0OlD6JX3UGNdbtP8bB8v9GOCKhNVE52X4yHL8i8RE7Ozv79O/z81t/xzORyLdCVn1om1atLXSz6/hO3TXPSbHE0D8pLQLRNI2qquwpLWJt/mHidTHIsvYTSqd37xfWeBYd1J6kLDq4PmwxCG1HnOV8ctI+IiXmamSpdmcTvZxAmuNWctI+xmbs2yzxVXm1P60EqPbuD3MkrVeN9wjF1V9pmlPgWopfqYxQREKro57qXNP0X5HI5C2WXxcPvd7QKgJaElEj3woNSejC5tJDmuZ0siaSbG6eTUlHK8tZdWwfZe5qzDo9AxLTOSu50+l6/fMzupFhc3C0sv5+yvW5qscADHLLqWvzKQrv793G2zt/YnvxmT399UgGNehSebvBxKTMXmGLy+mr4WiV9pM6d5W3/lUKITIshm50SXycTvH3U+XZjl+pRC/HYjP1Q5aatwNEsKel/pZCyyiDawlOVH2G1uzJr7ooqV5Nsm1SZIIShAC2bdvGkSNHKC0tZebMmY3uBywu/vVwtPpq6Vsbkci3QldkDuXN/d+iaHiznZ45DDnKnRl2lRXy1JZvWXl0b51Ye8YmcXPvc5jSpR86WeaBwedzx+plQb1uhs3BtdlDQo7roKuUrSX51Pi9JJisnJ3SOeSnHABVXg83r1zKt0fz6r2u+iUkfXD/r545dzImXfi+LT0h7iNwt+D9B4I2qqri9OZR5TuOhA67oRNWQ9OfZullOw7z2WGIMHyMutDOljDqOoQ5Eu0UfyG+qoX43Z+jKqUgmdEZBqG3zkY2DIhaowKPvyjEeaJOXjhJBZQQGj009FrBeP7551m1ahUAw4YNo1u3hg9M27hxIwCyLNO3b/M8PQwnkci3QumWeC7pOIgPjwTXez3ZZGdyp8ERjqq2Hwry+P23ixpsK7mnvIh7fvyInWWFPDhwLJd26U2Ju4pH133R6Dduhs3BW+OvJMGs/cCS744f4NXda1l9vHZfcrvBxNSs/tzU+xxMsp5yTw0WnYFkiy3gD09FVbn9q48aTOJBAkVF9QE6aOzlHhk2jvMzgjutMVgOgwWdJOHXuJkvwdTyTwMWGqeoPg46l7O/YhGlv2kj2cEygu6xV5JmG9VM0UVGovUi8kqfAA0r83o5jnjL6MgFFYCqKnhdT+F1vQC/OYTL59uHr3oRsvFszHHPI+mSIh6PLBlCnNcySzeF9mHYsGGnE/mlS5dyzz331Dtu7969fP/9yfbdo0aNwuFo/eeQiES+lXqw76UUVJeztrjx2s44g5VnzpqDwxC9fusHnaXcvPr9oHrDv7prLR1tsczpOZRrs4eQHZ/MqzvWs/LIPpQzks8Ek4UZ3QdwY5+zSLJoTzJf3vUD/9q8qt5rTq+bN/euZ+6+n3C7VfilbVZmTByzeg7kyu4DiDfX//e3+mgeKw8Hqq+VQJFAUVB1IOl+/XNJEiRYjTw38grOSe2i+c8ViFGnZ0xqb1Ye17Yz/4L01r9K0Z75lGp+OH4fx6vX1Hv9ePUajlevoUfsLAYk3t0i29KGwqTvQIJ1AiVVKwBQVKhQrJT7LfjQIaFilHzE6yqxSh4kCVJipjdbSZCqqngqHsFX9Xaj4xTPj1QXz8CS9AGSHNlTum3G7MCDwjhPaIukMLafDO7ilIEAAAAgAElEQVR1Jk+ezLPPPktlZSVvv/0248ePZ8CAAbXGFBcXc9ddd6EoCrIsc9ttt4UpxuYlEvlWyqQz8MxZ1/DKvq9ZdHAd5d7abeB0ksTolN7cmT2RTrboHkP82u61uLzB15w+v/17ZnYbhFGnY3hqJsNTMzlWWcH2kgJqfD7iTRaGpnbEHGLJyZK8rQ0m8WfyqX5kPfhrdCCf7Of+z5++5uXt63h93DQGJqXXmfPOzp81RCKDHxKsBpLsBjrbE5nRZRDnp/VEL0eu7OnKrLM0JfJGWcflUX6CI4SPqqqsLfhLg0n8mfaWz8cgx9A34eYoRBYdWfF/xlmznmJvJfm+OPy/OVm5RjVSoVgxSx66mW10dDTfD3O/+/OASfwpqn8/7vKHMMc/F9GYkqzjMciJeJXiwIN/EWPsjd3YP4JRCa1N+E52DU5iYiIPPPAADz30EDU1NcyZM4drr72WUaNGodPp2Lx5M6+99hpFRSdLx2699VYGDRoU1RgjRSTyrZhB1nNbz/Hc0G00q47v4ICrEK/qJ8lkZ0KHHFKb4YROl9fNktxtmuacqKnk8yO7uaTzr6eZptscpNua/sjLpyj8e0vwHRgkGWSdiuLRoeoVJBlK3FVc/fkC3hk/kwFJaeh+Sbrdfh+rAq7G15VuSuDjC6/VPC9Uw5O6Mj6tD1/mB5fM3549jjij9tIloWUorF7Hsapvgh6/s/R1ujmmYtZHvmwjGsz6jlhsf+BI0WuNjqtRjexzW+jjryBW1zynGXsr39A03l/zCYq/AFmXGqGITpbIZDhmk1f2f0HPyXBc12ae6git14wZM6iurubJJ5+kpqaGl156iZdeeqnWGL1ezy233NImTnQ9RSTybYBJZ+DCjAGBB0bB5uJjVPq0d4BYU5BXK5EPRq6zmK+O76HcU41FZ2BgQkeGJ2fV+oGy8tgejlcH1w/5FMmggEcHXhlUkDwy1QpMW7gQgywzrks3ZvcfSPfEBM215wAl1dWa5zSFJEn8Y9BUfIrC1wW7Gh17c4/RXNt1ZJQiEyJhf8UiTeNVfOQ6l9E7/oYIRRRdFZ6jrCt6K6ixbqWClfkPMyXz9agnooovD8Xzg8ZZfnxV72G0RzYJyYy9Gad7K8XVgZ9kptmvItV2eUTjEVoXlV9aR4bptbS49tprOe+885g7dy5r1qw53Sc+NTWVc845h5kzZ9bqI98WiEReCCstJTW15wV/YNGWkqM8vWMVawpz61zrEpPIjT1HckXnk10evjq2T3MskgyoKnK1DumX+jwVFQkJr6KwYv9eVuzfy5isLiffZTT+/Dfro/9tZ9YZeOqsmXx2bBvv5q3j55Jf25fqJImxHfpwVZfhDE3MinpsQvgoqpdjld9qnnfEtbLNJPI7y5eiEHzXpRL3Po5XbyLNGt3H7IovtN71qv9A4EFNJEl6+qQ8y/6SJ8h3LkSt5+9Tlixkxt5EZuxtYjVeaFG6dOnCQw891NxhRI1I5IWwijGE1rkgxhDcZrOv8vdw59pFeJT6u1Lkuor5y08fst95gj/ljKfco/00W9Un1UrigVq/P+XrvFxsRiOVeo+mZL5/UvO0utNJMhdl9OeijP4crSqloLoCnSzTyZogutS0EV7FFVIvdbe/LALRRJ+i+thdvlzzvF3lH0U9kQfth8UBqGp0WsPKkoEeiQ/TOe42jjvfp6xmHX61Er1sJ8FyHqkxU9DLzXM2idDCqYRvs2skjnZtY0QiL4TVgMR0bHqj5vKakR2yAo7ZU17IXevebzCJP9Pre3+gky0eqz6EVmoV+noT9/rUePxISKiG4N9tZvdu/g02GdZ4MqyR7X4hRF+oLQB1cvMe5BQuVb5iPIq2UjqAMk9e+IMJQAqxd72si+6J1kZdEplxt5DJLVG9ryAIwYnuCUFCmxdjMDGlS46mOclmGxMyAp9m+treNdRoOKjov7tWMzCxbqeZxqg+Cbw6VA3LAHq/LuhVg8Ep6QxJ0RaTIARLL1mxGzprnpdg0rY/paUK+WTXKK1yn0nW5yDpu2uep7dMiUA0ghBeqiqF5ZcQmFiRF8Luhl7DWZq3Leh6+T/0HYlRp2t0TKm7ik+PbNcUR2GNE4fRhFVvpCrYJwTVJ+MIdkUeQPGrmGU9NQGSgU72WF4ce1lE6klVVWXNkUN8sHMHR5zlyJJE59g4ruzTj0Ed0kQNazshSRLdHNPYVPz/aZrXzTEtQhFFl1kXh4SMqrFsxdoMHXskScJgvQZPxSNBz5ENZyEbekcwKkFoOhUpbO0n1TCdENuWiRV5Iew62+N5+dzpQZW13NT7bK7pMSTguE0lR4IqqfmtzaVHubbH0OAn+EN707h/8GgGNFD7LgEXZHZnyaWzSbWFv6Z00/F8Jsx7k9lL32fJ7h2sP3aUtUeP8N6ObUx9fwGT353LvpLge0ILrVtn+6UY5bigxyeYckg0D4xgRNFjkK10jjlX87xu9gkRiCYwvfUqZOPZwQ2WYjDFPh7ZgARBaHXEirwQEWenduaDCdfy9NbVfHl0T502jdlxKdzU+2wuzwquDCeUlpYn57n5y+CJ5DlL+PRIA60XT3WeacKmmi5x8Xw4cA6bT+SzeN928iud6GWZrrEJXNmzP53skelTvf7YEeYs+4AaX8NPA7adKGTa+wt4d+pMeiW2jV7hQsOMOjuj0p7i22N/wKdWNTrWqk9nRIcn29QTm95xl5PnCr6Pvk7Sc6D0f9lb8gh6yUKi5SyyHFeSaB4aoadnfipqvqbSvRlVrUGvH4Vd9aF6NzQ8SU7EHP8asqFttc0T2q5wtZ8UAhOJvBAxveJS+O+5U8mvqmDV0X2Uuqsw6w0MSsxgcFKGph+S9iC72tSdZ0Yny/zfOVPI3rmGV3b9iMv3m1aXZybxehWC74R5WufYkyugA5LTGJDc8GY0t9+HR/Fh05uQm5gkOD1ubl6+rNEk/pRyt5ubPl7Kl7N/hyFAGZNQm0+pJN/1EfmVn+D2FSJJBuzGnnS0zyDBPKxFJsGJ5v6cn/EqG0/8nRJ3fSVpEunW8xiS/OewHwSlqiouz1bcvmOADoshC5uxR1jv0Zh0yxB6OCaxt2JFUONjpAq8ihcAj1pDfuXn5Fd+ToJ5CENT/4NJF55N4aqqcsL5OoXOV/H4j/7mqkSyKYcEnQW8Gzj1hiTpumGwzUZvmYokN8+hVYIgtGwikRciLs3q4Ooeg5v0GoMTO2HRGaj2ezXNG5nSFQCdLHN731Hc2Gs4N3z1PmuO5yHpVCT5VAb/SzJm9kOlrvbXAhiW3pGu8QkNXi/zVLP44Cbezd1Inqvk5G10ei7M6MusrkPpn5Ch6c90ypJdOyitCb695qGKclbmHmBS9+glVa3dUedidhY/gV+trPX1Su9+jld+it3YiwEpT2MLYYNppMWZejGu49uU1OzgoGs51b4CJHTYjZ3pYr8MmyG0f3cNUVQPx50LOeacR7W3do90u2kQ6fZrSbZdHPEPPpIkMSr1PoAAybyKQ67GLNf/nlJSs5Ef8m9kZPqbGJrYZlFVFQ6W3EtJ5fsNxnLCvZViyUq3pDewGfsgSRaQ7C3yg6IgNEq0n4wqkcgLrYLdYObSTv14L++noOdkWOM4r0PtrhBmvYF3xs/kn+u+4dX9a/htsi7pQDUp4A5+1XrOgIbbSf5QmMsdaxfh9NZOuGv8PpYc2sySQ5uZnjWIhwdcyPaSAg67ypAkiY4xsQxMTG/0h/j8bVuCjvGUeds2i0Q+SIcq5rOzuPGaZKdnN2uPXc3Z6fOxGjKjFJk2CeY+JJgj25XGp7jYUXgr5TX1n1TqdP/MbvfPlNV8R4/EvyNJkX0qpJMMnJf6Z3o4JrGjbAkHXatPb4DVS0aMOLHIHvRS45tinZ697Cp5ln5Jf25SPMcrnmkkif+Volaxv+gP9E5bgUmX2qR7CkJzEh1nokck8kKrcX3Pc/jkyPa6pTEN+GOfMeikuvu5ZUniz8PH8Fbej/jUen6Q233glUFp+I3o1EmvM/v248IGEuONxYe4ac38Rjfpqios3LuZj/btocJTex9Aj9gk5vQawszuA9HLtf8cfkVhd3FRg6/bkJ1FJzTPaY+cnr3sLP5HUGO9SgmbC//E2ekL2+Xqqaoq7DpxZ4NJ/JkKXO+jl+10TfhLxOOSJIl06xDSrUPwKlVU+04eevXjsTm4leCfZB12fkjvhDvRy6EdmuZXKimoeCXo8Yrq4oTzDTrGB9/NRhCE9kt0rRFajayYRF4450ps+sCH3vy/PudzWWb/Rsc01PJSkoF4D+gbX62b1rcvj4+dUG/ypqgqD274MGAS73Pr8Ht1dZJ4gL3lRTy07jNu/uZ93L/pn1/vB5AguP0+1hTk8eC65dzwzbvcvHoR/978NYdcpSG9Xlt1uGI+Wk7erPBspdyt/QlJW1BWs4bS6uA3lx6teJMa75EIRlSXQbbiMKZT4zuAWynUNNevVnHM9XnI9y6tWoqiajukqtj1HopSHfI9BaG5qWH6JQQmEnmhVRmWnMW7Y27goo59611tH5jQkefPvpJbsgO3oOsd1/Cja0kHxHshzgMmP8gqSCroFFSTHynWz19GjWlww+p3Bfs5WFnS6P39Hh2qIhPo7WrV0f38ac3Htb5m0umxGzVuADYoeB1VXPPVfN47sJmv8/fz5dG9/HfnGsZ+/F9uXv0+xTWVgV+njfMrNRxzfah53hHnexGIpuXLd87TOEPluGthRGIJpDLEDxBVvtA/eDhrftQ8x686qfJqOzdDEIT2SZTWCK1Od0cy/xk2lcJqJ6sL9lPurcaiMzAgIYM+ccEfXz6r+2A2FjX8A1qSAKMKxl9XwxWfhKpIXJDZk1iTucG5yw41vjqr+CUU/6kkPnA5xkcHd3JTn7PJSfy1V/0lPXqxYHuQq8BGP5LDh7eBW6nAl0f3sK+iiHfHXUOSObQygragxn8cv6p9NdTlPRCBaFo2VfVRUvWV5nnFVV+SFX9vBCIKJLQ1Pi0nPf+Woob24VhRGm8dKggtlQoQxhr59lewqI1YkRdarRSLnalZA7m+xzlc1XWopiQe4MJO2XSwBN+NQlXhVEXLNT0b78KTX13e6HXFd+pbL/i3qLl7a2/0nd1vQHATZRXJ4SOY8u08Zwl3/bAs6JjaIlXVfvBYU+a1Zj6lEpXA7U9/y+svi0A0gVn16b/5iooeP0bJh66RUqq684Knk0LreKM7o1OOy72D/SX/YueJu9l14k8cKnsRt09biZAgRJWorYkasSIvtFsmnZ6Xz5vOrJVzcfk89S6Oq+rJlXlVPbkaDxJX9RjAyLTG2w0G6hGvhHCC7JdH9tb67z7JKczuN4C5Wzc3Ok8y+4NK4k9ZU5DHjtIC+sS3z64ZJl0SoZwQZtanRCSelkwnNfxUqtF5siXMkQQn2ToCky4Jn7+QBF0NSXINJvnXD2CVip4iv4UyxXT6aHhZMpFuuyDke9otoympWqJpjl5OwmLsg8u9g70l/0OFu263rryyZ0i2XUj3hIcx6ESPeUFor8SKvNCu9Y3vwHvj59A3PrXexXFJOrkKr/gkUCWu6TWYx4ZPDNidJCsmscFroZ54V+6u22njkfPGcmWffo3MUk/2xtdo3r6Nmue0FQZdLMnWMZrnpcVMDn8wLZwsm7AZe2ueZzcNjEA0gcmSgSzbSLKNJWToK2sl8aoKNtlHZ4OTnoZS9Jy81jHmEgw6R8j3jLdehF5u+JyJ+iTGzMTp3s6m47PqTeIBVHwUVn7EpuNX4fU3vh9HEKJKlVDD9CucJTptlUjkhXavV1wKyy64nkXj53B55xw62+IxSXoUP/i9EkaMXNltAB9dfB2PDb+gTivI+kzLari3fKgdCi16Q52v6WWZJ8ZO4M3JUxnXpWutzyJ6WWZct66E0rJ7e2lBaEG2EZ3sMzWNN+lSSLGeH6FoWrY0+9VRmRMOXu9+rJ73MUh1P02f+X1pkf10M5RjN2TQO+HOJt1Tlsx0iA3+NfRyEvHWy9lWeAt+NXCdfJV3HztP3NOUEAVBaMVEaY0gcLLn9OCkjgxO6nj6azV+H16/nxiDUXN/8AHxGfSLT2dr6bH67yerqI30qa/PkOT6T+KUJInRnbMY3TmL4uoqjrucSEhk2B2UeKv4avkuTfcB6rS7bG+SLKNIs11CfuXHgQcj0zfpb8hS3Q9a7UGKbTKHyp7D4z8e1HiHaTAO05AIR1W/soq/oarB1edbZD/9bEMx6uKafN/kmOvw+vIpcL7Y6Di9nED3lLcorv4WnxL8PoLSmu9xundgN0X24C9BCFaoT55/S6zHByZW5AWhAeZfWjyGcsiPJEk8OXQKccb6a4F1AXrU12d2gA22AIkWK32TU+mTnEKs2UyCKbRa5ASTNaR5bYUkSeQkP056gHIZWTIxIOU/JFtHRymylkcnW8lJfRW9HLhO26LPonfy881ycJbPd5iami81zampXoqqBncAXWMkSSIj/s90SXweq7HuJnVJMpFom0GvDh9iMeRwzLlA8z3yQ5gjCJESttIaISCxIi8IEdLFnsj8865j1jdvUeat/Yhc0qlIkhr0G1V2XApj0rtpjiHWaGFEahZrCvI0zbuoU7bme7U1smQkJ+kJMmKu4LBzIQWVX57u0GLUJdLRPoNO9umY9R0CvFLbpihuXJ6dmHTp+JQK6tskLKEn0TqR7omPYNBpqxcPl6rqpWjdwKwoRdS4v8NiHheWGOJtlxJvu5QqzxYq3VtQ1Rr0ukQc5vPR/7Ly7/GXUOM7rPm1K9yNb3oXBKFtEom8IETQd/kHKSz3IMk6dAYFSVLhly44yAr4ZQI9PMywxfLq+dPQBVGbX5/Z3QdrSuSteiOXZeWEdK+2RpIkEizDSLAMw6+48SqlSJIBoxyHFMrmgzbG5dnJjoKbcPvzGxwTY+xPdvKzWAz1l4ZFi98f2r4Pf5AlQ1pYjf2xGus/eVpR625qD0ao8wQhIsRqetSI0hpBiJBKr4d/b/oGkFAVGZ9bj7fGgLfagK/GgOrX0VgSr5dkLu7cmyWT5pBhC7293PiMnoxK7RL0+PsHnE+MQeOpse2ATjZh1nfApEsUSTxQ5dnHluNXN5rEA7g8WzhU9h/UcBXNhkoKbd1KivLeB0MQJUr10Yehll8QhNZHrMgLQoQsy9uOy+vRNCfBZGVSZi86xcQxpUtfUq2hHSZzJp0s8/yoK7j1uw8Crszf238Ms3s0z0ZEoXXZU/wAfqUiqLGFlctItF5Akm1ihKNqmEHfK6R5en3PMEfSOJ1sI848grKaNZrmJVknRCgiQdBGVcO32VVVxYbXQMSKvCBEyOeH92ieU+Ku4o5+I7ml79lhSeJPiTGYeH30lfxr2MXkxNeu6dZLMhd1yubdcddwa58RYbun0HY53dtwujdpmnPM+U6EogmO1TIZSYrRNMdgyMFoCPIE5TDKsM/SNF7CSFrM1AhFIwhCSyZW5AUhQkrd1SHNK3FX0SGMSfwpBlnHtK4DmNqlP7nOEgqrXehlmSx7AklmW9jvJ7RdBa4PNM8pr/mRGu8RzIaOgQdHgCzbsNmuxuV6Keg5dtvvm6XDTqJ1HHHmsymr+TGo8Z3jbm22TcSCUK9mrqRrT8SKvCBEiFkX2udkiy6yNbmSJNHVkcjZqZ0ZmtxJJPGCZm7fkZDm1fiPhjkSbWId92M0DgtqrM16FVbr9AhHVD9J0tE3+TkcpsAtZzMc15EZe1sUohKE4In2k9EjEnlBiJCBSema5ySarHSMCX1jqyBER2g/YKVmrnaVJQvJiQuwWi5vZJQRe8ztxMf9u1lW40/R6xwMSH2L7gl/xWroWud6nHkEfVNepHvCn5s1TkEQmpcorRGECJnVYxAv71irac6VPQZikEVHFKFlM+szQ5zXPGU1Z5JlK4kJ/8Xhu4/Kyrdxu39EUauQ5Vgs5vHYrLPQ6ZKaO0wAZNlEhmMO6fZrcHm24fYXIKHHauiKxRDa/wNBiApRWhM1IpEXhAjpbI/nwsxsPj20K6jxVr2Bq3sMinBUgtB0qfapHHO+qWlOnHkUJr32p1SRYtB3IS72keYOIyiSJGE39cNOv+YORRCEFkaU1ghCBP3r7IvolxD45E+jrOOF864g3eaIQlSC0DQxxt44TGdpmpPumB2haARBaFHCVB+vqpI4WCoIIpEXhAiyG00smHA1M7r1b7Bkpm98KgsmzGJ0et06WEFoqXomPYFBDq5TSoeYK0mwjItwRIIgtBhqmH4JAYnSGkGIMJvByL/OuZj7Bp3PBwe2srusEI/fT7LFxqVZfRiYmC42qwmtjsWQRf8O89leeDM1voMNjkt3XEfX+AfFv3FBEIQIEIm8IERJotnKTX2GN3cYghA2VmN3hmSsoKRqJfnOBbg821BUNwY5gSTbJNLss7AYspo7TEEQok58cI8Wkci3M8WFFXy+eAP7d+bj9fiIS4zhvEn9GDSiO7IsKq0EQdBGlgwk2SaRZJvU3KEIgiC0OyKRbyeqKt288NiHfLV8E4q/duHZ54s3ktYpgdv+Opmh5/ZspggFQRAEQWgTRH171Igl2Hag0lXDA9e9ysoPf66TxJ+Sf7iER257i28+3RLl6ARBEARBaFPEZteoESvyzSAvL49HH300qvdM6AvD+xoDjvtq7WK+Wrs4ChEJQmRE+3tLEAShOeXl5TV3CEIzEom8IAiCIAiCEB4q4ev/LlblAxKlNYIgCIIgCILQCokV+WaQlZUVtcf/D930Jhu+26N53nMf3E633i3nOHVBCKczv/9EKY4gCK3Z/v37KSwsbO4walHFSnrUiBX5Ns7t9oY0z+PxhTkSQRAEQRAEIZzEinwAXq+X9957j48++oi9e/fi9XpJTU1l5MiRXHPNNXTr1q25Q2xUXIItpHmxcaHNEwRBEAShnRMr8lEjEvlGlJaW8vvf/56tW7fW+vqhQ4c4dOgQixcv5m9/+xtTpkxppggDG3lBDqs/26ZpTtdeaaRlJkQoIkEQBEEQ2rRwbXYVAhKJfAMUReGOO+44ncRPmjSJK664ArvdzsaNG3nppZdwOp389a9/JS0tjbPPPruZI67fiHF9iE+MobTYFfSci68ajiSJb0JBEARBEISWTCTyDViyZAnr168H4Prrr+f+++8/fW3w4MGMHTuWWbNmUVZWxt///neWLVuGLLe8LQcGo57f338R/3vfe0GN75GTwfjLBkU4KqE5bS8qYO6OTaw+ehCnx02MwciI9Eyu6TuI/skdmjs8QRAEoTVTQQpXaY0o0Qmo5WWeLcSbb74JQFJSEnfeeWed6926deP2228HYM+ePXz77bfRDE+T8y8ZyG1/vZRAi+w9+mbwP/+9FqPJEJ3AhKiq8nq49YtlXLz4bRbs2sIRZznl7hqOuipYtGcbk5e8w/UrPsDpcdeZ63H7KClyUelyo4p2BIIgCILQIogV+Xrk5eWxZ8/Jlo0TJ07EbDbXO27KlCk88cQT+P1+VqxYwZgxY6IYpTaXzjqHnv06seydNaz+bCs+r//0tU5dk7l45nAmTTsLk1kk8W2R2+/j+hWL+TH/cKPjVh06wJxPFjH/kisxSjrWr9nHR4vWs/HHfafbiWV2SeaSaUMZf9EAbDGmKEQvCK2XX1FYv/UQuUeL8fv9JMXHMGpwN2Ks4ntHaMPEek/UiES+Hj/99NPp3w8bNqzBcTExMWRnZ7N9+3Z+/PHHaITWJL36deS+/53BzQ9ezOH9hXg9fuISbWT17CBq4tu4lzavC5jEn/JzYT7//u5bXO8dZdP63DrXD+We4IUnP2XhG6v5n6dm0SM7LdzhCkJE+PwK3/98gKVfbWHfoSJ8fj8JsTbGn92LyWNySIwNX7cuv6Lw3oqfWfTZT+SfqKh1zWIyMHFUb34/bQQJYbynILQMUhg3u4rcJBCRyNdj//79p3+flZXV6NjMzEy2b99Ofn4+lZWV2Gwt/005Nt5G7NAuzR2GECVexc/cHZuCHi/5VFb950eM+Y2fJVBS5OL+297m6ddvIDMrqalhCkJEHThSxH1PfciRgrJaXy+tqGb/4SJeW/wDt0wfydUXD23ywobP5+ehZ5fz9fq99V6vdntZunILP27O49m/TKdjalyT7icIQvsVlRp5n89HUVERXm/gw4lyc3NPbzJtLgUFBad/n5bW+Grjmddb2slqggDwzeFcCqsqgx4ft9MXMIk/pdJZw/P/+iTU0AQhKvKOlXDzY+/WSeLP5PMrPLdwNa8vbfrT1aff+brBJP5Mx4squOd/F1NdE9rBfYLQYqlh+iUEFNFEvqSkhHvvvZchQ4Zw7rnnMnjwYP7whz+we/fuBue8/PLLzJkzJ5JhBVReXn7694FW2C0Wy+nfO53OiMUkCKHKKy8NfrCqErdD26m+mzbkcij3hMaoBCE6VFXlkRc+wVlZdxN3fV754Ae27csP+X6FxU6WrNwc9PhD+aV89v3OkO8nCEL7FrFEvqqqitmzZ7N8+XLc7pOdLrxeLytXrmTatGnMmzcvUrduMo/HA4BOp0Ovb7z66MyNsKfmCUJLomjoMmMuVDBWaF8GWfnpFs1zBCEatu7NZ3eetqel738RfCnaby37aguKou176IMvNoluUELbEa7VeLEqH5SI1ci/8cYbHDhwgD59+vDwww+TnZ3N4cOHeeutt/jggw94/PHHKSgo4O67745UCCHT6XQAQdVJnvnmKzaMNq9jB4v5cvEG8g8XA5DWKZHxVwwhvXP7rt9Oj3EEPVZfGdq7ZlGheBoltEwffr018KDfWLl2N/deOzakzjLrth7UPGffoROUVlSTEGvVPFcQWiSRgEdNxBL5zz//nJiYGF5++WWSkk4mUj169ODxxx9nwoQJ/OlPf+KVV16hrKyMv/3tby0qCbZaT76Z+nw+/H7/6cS+Pm73r49rjUZjxGMT6io8VsqzDy9mwzd1S7YWvLCSoef14tDu53QAACAASURBVPb/uYLUjPhmiK75jevcjViTmXJ3TeDBIX4b6nQt5/tXEM50+HjDdfEN8foUCoqdISXylVWhPZmtrHKLRF4QBM0iVlpz6NAhBg0adDqJP9Po0aNZsGABaWlpLFq0iLvvvhufT1tdbiSdWRdfXV3d6Ngzr8fFic4D0XbsYBF3TXuu3iT+lA3f7uau6c9xpJ3WcVv0Bmb0yglqrCcutLeEju38qYfQcmkpLQvHPJsltAUdm1UsBAltiCqF55cQUMQSeVmWiYmJafB6t27dWLBgAV27dmXFihXcdttttVa3m1N6evrp3+fnN77p6dR1SZJITk6OaFxCbX6fn0dvepOSE4HLOkpPOHn0pjdqHYTVntwxeATZCYGTbU+8jJKhLaHQ62UmXDwg1NAEIaI6JNo1z5EliZT4hn9+NWZI30zNc7pkJBLvEKvxgiBoF7FEPiMjg717G2+/lZqayvz588nJyWH16tXceOONuFyuSIUUtB49epz+/aFDhxode+p6RkZGgyfACpHx48odHD4Q/Ca2o3lF/PDl9ghG1HI5jCbmXjyDQSmNt1Ptm5jC7TdO0PTaoyfkEJ8YWtIjCJF20bl9NM8ZOagrsXZL4IH1uGxsf2SNpaJTLxjYospLBaGpJDU8v4TAIpbI5+TksH//fnJz654MeabY2Fjeeusthg0bxvr16/nyyy8jFVLQ+vfvf/r3GzZsaHCcy+Vi165dAAwdOjTicQm1LZ+vvd/z8vk/RCCS1iHJYuP9ybN4deIUzuuYhe6XxEGWJEakZ/LihMtYNuUaJl88hIumDA7qNTO7JHPrvZMiGbYgNMnwflmaD1yaOj70J0xpyQ4uGRNcKRtAenIsk0Zp/7AhCIIAEUzkx44di6qqvPnmmwHHWq1WXnnlFcaPH98iWnB17NiRnJyTb8TLly9vsK3kkiVL8PtPlmpMmKBtFVNoul2bG39aUp/dWw5HIJLWQyfLjO/cnbcvms7eG+9h++/uZN+N9zD/kiuZ1KUnellGkiTueOASZswZiSw3vEo4YGgW/375OuyO0FYu2zNF9VDl3oizehWV7rX4leZ/EtlWybLEQzdPxKBvuGnBmS4dncPwfp2bdM97rhvLiIGBT89Oirfxn/uvCLmuXhBaJNF+Mqoi1rVm1KhRPPbYYxgMhqDGG41GnnnmGebOnUtFRUWkwgra7NmzeeCBBygoKOCf//wnDz/8cK3r+/fv57nnngOgc+fOjBkzphmibN88bu2nIXpqvKiqKh5jc3Il3maoP4GQZYkb7hjPZVcOY8Wyn1jz9W6cFdWYzAZ69+vIJVOH0qtvRpQjbv28/hMUu96k1DUfn/Lr5mtZshFnnUqS/UZMhq7NGGHbNKBnBk/9aQoP/N9HuKoa3os1ZVx/7pkztsnvD0aDnn/dfRlzP17PB19spqi09gc1g17H+HN6ccuMUaSEUMMvCIJwSsQSebPZzPTp0zXNkWW52U91PeXyyy/n/fffZ8OGDcybN4/Dhw9z1VVXERcXx88//8yLL75IRUUFsizzyCOPBDw4Sgg/R5yV0iJtK5n2OKtI4jVISnEw+/djmP37Mc0dSqtX49lJ7onZ+JSCOtcUtZKSyrcprVpEZuKLOCzjmiHCtm1o30wWP3UDn6zewZKVmzmYf/LEY4vJwPizezF1/ACyu6SG7X56vY7rLj+b2Zecxfc/HyD3aAl+v5+k+BjGnNUj5Bp8QRCEMzU5+3zyySf505/+FI5YWhRJknjuuee48cYb2bZtG99++y3ffvttrTEGg4FHH32UkSNHNlOU7ds5E3L4ZIG2OvlzJvSNUDSC0DCv7xi5J2bVWoWvj6pWc6jo93RNWYTVNCRK0bUfDpuZmZMGM3PSYLw+P16fH4vJENEP93q9jtFn9WD0WRG7hSC0OGKjavQ0uUb+tdde44EHHjhdK96WxMfH8+677/LII48wZMgQYmNjMRgMpKenc8UVV7B48WKmTZvW3GG2W5fMOieEOSMiEIkgNK7Q+XzAJP4UFQ/Hy/8R4YgEg16H1WwUT+gEQWjVwlIPsmzZMkpKSnjmmWfaXAtGvV7PrFmzmDVrVnOHIvxGl+w0Lph2Fp+/vz6o8eOmDKFbn/TAAwUhjPyKi7LKDzTNqXSvpcazC7MxO0JRtT5+v8KGzQc5dLQEv6KQmuTgnKFdMZuC24clCEIUicOcoqbJifyYMWP4+uuvWb16Nddddx0vvvhi0CecfvPNNzz11FMsXbq0qWEI7dTtf5tCdaWb1Z9uaXTcyIn9uPPxqVGKShB+5apZjaJq70pTXv2pSOQBn8/Pex9tZPEnP1NwonYjBHuMmYvH5TBn+jnYY9rWIpIgtGqitCZqmlxa88ILLzB9+nRUVWXz5s3MmjUr4GmomzZt4pprruGWW25h9+7dTQ1BCBNFUVAUpbnD0MRg1PPA07O498kr6TWg7omKPft34u5/zeDPz1yNwSg2JAvR51dKQpxXHOZIWh+3x8cD/1jCf9/6pk4SD+B01bBw2QZue3A+RSWihacgCO1PkzMbWZZ57LHHSElJ4fnnnyc3N5eZM2fy6quv1johFU62bPzPf/7DqlWrAEQbwBagKL+MFfO+54t3f+TE0VIkCVIzE5l41QgmzhpBXFLLb40myzLjLh/CuMuHcHDvcY4fPpk4deiUQOceHZo5OqG9kyRTiPPECvOTL3zG2p8aP1QQIO9wMfc/vpiX/vdq9EH2ixcEIULC2f9drOwHFLYDoe644w4ee+wxZFmmoKCAq6+++vSpqMePH+fBBx9k8uTJrFq1ClVVUVWVQYMG8cYbb4QrBEGj5W+t5nfDH2be//cJhUdKUFUVRVHJzyvizSc+5NqhD7Hqg3XNHaYmnXt0YPjYPgwf20ck8UKLYDGGdkqo1TgwzJG0LoeOlvDZ1zuCHr/nQAGr1+6LYESCIAgtT1hrDaZPn05SUhJ33303FRUV3HDDDVx44YV8+umneDye06e29u3blz/+8Y+MHj06nLcXNFj+1mqee2Bho2M8bi9P3v4WsiwzZsrQKEUmCG2L2dADm+kcKt0/BD1HL6dgt1wQ9lg8Xh9fr9vLtt3HqPH4cMSYGTWkGwOyM1rc09GlKzZpnrPk0585f2SvCEQTPS5XDWvXHaC0tBKDQUeP7qn07p3e4v7/CEJDJMLXflL8qw8s7EXD559/Pi+//DLXXXcdbrebZcuWnU7ge/TowR//+EcmTJgQ7tsKGpw4Vsp///pe0OOfvmceQ87vgz3OGsGoBKHtSrLfoimRT7RfjyzVf+puKBRFZe6H61i4fCNlFdW1rs3/aANdOyVx29XnMmJQyzlVdt3PeZrn/LztMB6vD6Oh9e2HOXGigrff+Z4vv9yB+zenVnfrlsL0aWcxYUKOSOiF1kGUxERN2EprAPx+P++99x733XcfiqIgSdLpOvgpU6bw0UcfiSS+BVgx73v8vuA3tbqrPXz5nraDlwRB+JXDMo5UR3AH58VaJpNsvy1s91YUlcdfWMGLC76rk8SfcuBwEX/61xKWf70tbPdtqsoqd0jzqqo9YY4k8nLzTnDbbW+zfPnmOkk8wP79hfzzX8t59rkvTi+MCYIgQJgSeVVVWbp0KZMmTeKRRx7h+PHjqKqKw+HA4XCgqioffvgh8+fPD8fthCb68l3tSfkXIcwRBOFXKbF3khH/JHo5td7rshRDsuNOOiU+iySFb41l7rJ1rFgduNZcVeGJlz5n+97Gu479/+yddXgUV9uH79mNuwMJHtydoAnuXmixOvRtS0vdhXrf2tuWtl+NUsOhFAju7gQIEJwQIUbcNlmZ748QSIjszMbDua9rW9g5Z86zJDvzm+c8Ulk42Fu2I2FvV347GZVBamo2r7+2nEQFVXf+/fcES5aKa7GgBiCX00tgljLvP27YsIH58+cTHh4O5Il6Ozs7Zs6cyezZs7lx4wazZs0iPj6eDz74gPj4eJ577rmyLiuwEKPRRHx0sup58VGWldATCAR38HCairvjfaRlbyYteztGUyoajQOOtgG4OYxHq3Eq1/Vy9QYWrT0KJvnOTVHKfxUN0TCZZBavO8ZHL4wpVzssoUv7hkREq7vutGvpi20NKzMbHBxCfEK64vGLFx9kwviu2Fv4oCMQ1Hb0ej3Lly9n3bp1XLp0Cb1eT506dejTpw8zZ87E39+/qk0sV8p8xXvhhRduh9BYWVkxceJE5syZg4+PDwAtW7Zk6dKlPPbYY1y7do2ffvqJhISE2xVuBJWLJHH756V2nkAgKDuSZI2rw2hcHUZX6DpRMcm8/OlqMtJ1hRPGbnm6ZOS8Pdm7vtx7jl3mZnIGXu7l+1ChlvHDO6lOeB0/omZV+jEaTawLVvcZs7Jy2brtLGPHdK4gqwSCMiKXX7KrWq98cnIys2bNIjQ0tND7ERERRERE8M8///Dee+8xYcKEcjKw6ik3JT18+HCCg4N5//33b4v4fHx9fVmyZAkdO3ZElmX++ecfnnrqKXQ6XXktL1CIRqOhTkNP1fPqNvSqAGsEAkFFEB6VyOw3FhMRnVTsfVDmVjUIE3kxNQUwGk2EXYmteCPN4N/Ym/4Bzc0PvEWj+p4E9a5ZFWsiIhKJjy/a6MocR49erQBrBIKajclk4plnnrkt4ocPH87PP//MkiVLeOmll3B2diYnJ4e33nqLQ4dqT4hamYV8r169WLFiBV9//TWNGzcucZybmxt//PEHgYGByLLM7t27efjhh0lJSSmrCQKVDH2gl+o5w6b1rgBLBAJBeWMwGHnl09Wkpucltha3mSYV/H8xYj67mITLquDNuSNp39rP7Lh6Pq58/vakGhdWk5lpWUKvpfMEgkpDlsrnpYLVq1dz9OhRAB599FG++eYbAgMD6dKlC7NmzWLZsmW4ublhMBj46KOPalwn+5Ios5BfuHAh7dq1UzTWzs6OH374gQkTJiDLMqdOnWLq1KllNUGgkuHTe2Ot4obn4GzHgEndK9AigUBQXuw9epnoWOUOEgmKbF+7ONqXq02W4mBvw//mTWb6pJ64OBXtdGtjrWXkoHb8+Nl06tVxrQILy4alce4iPl5Q7amCZNfff/8dAC8vL+bOnVvkuL+/P3PmzAHg4sWL7NmzR+WHqp5UuvtCq9XyySef4OXlxS+//HI7SVZQebh7u/Dcl9P5/Jk/zI6VNBIvz38IR+fqcWMXCASl8++WU+onFbhhOjnY0kmBF7yysLW15j8z+/PIlF7sPXyZyBtJGE0yPp7OBPVugUsNvjY1bOiJu5sDySlZquZ16tSwgiwSCGom4eHhXLx4EYBhw4ZhZ1f0wR9gwoQJfPLJJxiNRjZt2kRQUFAlWlkxVNk+5IsvvoiPjw+ffPJJVZlwTzPwvh4gwbcvLyGnhLrLDs52vDz/IQKGdahk6wQCgaVcuBpXpvmjgtpiZ2tdTtaUH7a21gzu37qqzShXrK21jBzZkUWLlTcLs7W1Yri4JguqOeWW7KqQEydO3P5zjx49Shzn5OREq1atOHv2bK2Jk6/SgMKZM2fi5SWSKKuKgZN60H1gW7YtP8yWZQeJi0xCksC3sTfDpvVmwKTuwhMvENQwcnINqudI5JUOdnd1ZOrobuVvlKBExo3rwrrgk6SV0KzrbsaP64qzc/HeRoHgXuXKlSu3/1xaviZAw4YNOXv2LDExMWRmZuLo6FjB1lUsVZ4ZNGLEiKo24Z7G2d2RCU8MZMITA6vaFIFAUA64uzoQd1N5XXLIi6xxd3Xkqzcm4uPpXDGGCYrFy8uZjz68j1dfW0ZWVuldaQMDW/H444GVZJlAYCHl2cxJ4Xni4u7sRNarV6/UsQWPx8fH06RJE4tMqy6IQu4CgUBQiwjs2UL1HP9GXiz8dAYtmxTfdVZQsbRt68d38x+kV69maDRFK3V4eTkze/YA3n5rHFqtuG0Lqj+SXD4vpaSmpt7+szkPu739nUiD9HR1To/qSJV75AWCyiYjXceqRQcI2X+JXJ0eZ1d7hk/sxoCRHavaNIFCoq7Es3npQSIvxWI0mvCq68qAid1pH9AM6R7vXjZhWEeWrz+uas67z4wSnvgqpnFjLz768D5i41LZu+cCySlZ2FhradasDr16NRMCXiAohdzcvN0srVaLlVXp0rZgImz+vJqMEPKCewadTs9nb6/i4MZQuKtO9undF/j27VXMeHYokx7qW0UWCsyRFJfK1y8t5uiOc0WObVp8kMat6vHsf6fSulvN3iotCw19PZgyqqtiMT9uSAeaNfauYKsESqlbx5XJk0tO1hMIagSVnOyq1WoBFDlyCna2rw2OH/GIL7gnyM7O5ampP3Dg3xOQoy9yjZEBXZqOXz9cy/z3VleFiQIz3IxJ4YVxXxUr4vMJPx/Da1O+JWTvhUq0rPox58FAxgxqb3bc8P5teOGxQZVgkUAgEFQcDg4OABgMBoxGY6ljc3LuNFSzsan5PRmEkBfcE7z/8lJunIku3NGyAPl/l4ENfx9kw7LDlWecwCyyLPPxE78RF5lkdmxujoGPZv1KisqEz9qEVqvh1f8M5dNXx9OjY+Mix7u2a8hHL43lrWdGYGWlrXwDqykGg5H4+DSibySL7qkCQVmo5IZQBePis7NLrwBV8Libm5vyRaopIrRGUOuJjUkhZNf5YlvV303+mD+/3sKIKT1qxbZbbeD88XDCjl9TPD4zXcfmJQe5/5mhFWhV9UaSJPp1b0a/7s2Iu5lGTHwqsgz1fFyo613zuqBWJLGxqawJDmHDptO3y0BKEgT08Gf82C5079ZEXAsEAoVIVVC1xtfX9/afY2JiaN68eYljY2JigLxrpLd3zQ8rFB55Qa1nxZ/7Iceg6rqSejOd04evmB8oqBTW/7VP9ZwNf+0rFAt5L1PHy4VObRrQuW0DIeLvYufuMB587BeWLj9cqJa7LMPBw1d49c0VvPfhGnItqM8vEAgqh4LCPSIiotSx+cf9/PxK7ABbkxAeeUGt5+Thy4q88Xezf+tZOgY0K3d7BOq5HBqpek58dDJpyZm4ejhVgEWC2sDBQ5f58ON1mMw88O3eewFZlnn3rfHFlocUKCMlI5vgfWfZcCCMhOQMNBqJJr4ejO/fnoHdmmNjLSSJwDI6dLjT7fjYsWMMGlR87k9GRgbnz58HoFu32tH8TnxrBLWe3GzLPGkZabpytqR2Enc9gc0Ld3LjSiwmkwmfBl4MnhlI47YNym2N3LuqDCmep7NsnqD2YzAY+eqbzWZFfD579l3kwKHL9O1d8pa9oGRW7z7NF4t2kqsvnIiYlJbF8fNRfLt8D/+dM4b2/r4lnEEgKJn69evTrl07zpw5w/r163n++eeLTWRdvXr17WTYIUOGVLaZFYIIrRHUehycbC2a5+ruUM6W1C6SYpOZN/EzZjZ9mr/eX8H2RXvZuWQ/yz5bw6z2L/DSwHlEXogul7XcLKhxrtFIOLvX7Nbbgopj/4FL3EzMUDVnbXBIBVlTu1mx4yQf/76tiIgvSEJKJk/+dyVnrsRUomWCCqOSk10BZsyYAeR1ef3000+LHL9y5QrfffcdAI0aNSIoKEjlh6qeCCEvqPUEjuig6pqQP67PkLYVZFHNJyEqkbm932T/v0dLjEM/tessc/u8xZVT4WVer++oTqrndB/UFjv7ml9aTFAxbCuljGlJHD12jZSUrAqwpvZyPTaZL/7eqWhsjt7AG/+3HoPRVMFWCWoj48ePvx0us2jRImbNmsWOHTs4ceIECxYs4IEHHiAlJQWNRsO7775rtnFUTUEIeUGtZ9z9PdHY2yiOk5cAz3putL1Hmwrl5ugJOXCJXcEnObjtLAkxKYWOy7LMvImfExueYPZc6UkZvDXmU3RZZSvlN2RKT2xs1V10Rz/Ur0xrCmo3iUnqvPH5JCVnlrMltZtVO08pDl8CiElMY9+pqxVokaAykOTyealaU5L47rvvaNeuHQB79uzhySefZOrUqXz22WekpaVhbW3NBx98QJ8+fSrgU1cNteNxRFBtuHQ6guO7wshM12HvYEuHPs1p271puZduu3o2ii1LDhJ9LR5k8PZzZ8j9AbTqWrRMnKOjLWNm9mbNz7sUi/lZr46858rNpSRm8M/CvWxeeYy0AmJF0kj0CGzFxEf70aFHU0J2nOHiMeUVfW5GJbJzyT5GlKHxkLO7Iw++MppfP/hX0fieQ9rRJbCVxesJaj9arWV+LCsL51UFJpNMRFwyaVk6HGytaVTXA+tK7BtgMJoI3ndW9bw1e84Q1EUUGqjRVFHBMHd3d5YtW8by5csJDg7m8uXLZGVl4e3tTUBAAI888ggtWrSoGuMqCCHkBeXCsZ3n+PvLDVwIuV74wBfQuLUv0+YOp9+YzmVeJ+b6Tb567i/OHLpc5NjGv/fj364Bz/9vOv7tCida/uelEcTHpHBw3Ukk8q4xBWV6wb/PmDuUQAtCOWoyUVcTePOxBcTfSClyTDbJHN4ZxpFdYTzxxhhOrTuo+vzr/m9zmYQ8wMQnBpKVoWPx/zaVOq7bwDa89sPDaDQ1R3AJKp/Gjbw4c1ZdDoednTXe3urzNSqbjKwcVu8NZdWuU0QlpN5+393ZnvH92nNfUEfqeFT850jNyCbdgt24yLjkCrBGcK9gZWXFtGnTmDZtWlWbUimIO52gzGxafIB3Zv5YVMTfIjzsBh//5zeWf7elTOtEXYnjhdFfFCvi87lyJpKXxv+PCyHhhd6XJIl3v5rGY2+NxamYG7EEePm588b8mUyfM7hMdtY00pIzefPx4kV8QWQZfvxoHaePFf9zLo1LJ65hNJTeNtsckiQx86VR/HfFs/QZ2RHNXZ7RVl0a89I3M5m3cDZ2DpYlOAvuHUaPVP+wPmRQW+yred5FRFwy0z/4m29W7Ckk4gGS07NZuOEID8z7kxMXoyrcFkudsqL/Qw2nvBJdy7OxVC1GeOQFZeLU/ot8++pSRRfehZ+so15jb/qNVu+ZNxpNvP/wT6TcTDc7VpeZw7yHfuS3g/Owdyzc7GHyQ32Z/FBfThy6zJ5NoWSl63DzcGTI2C40b19ftV21gbV/HyAuurCILy2oKEtrWQONnOxcHJztLZpbkA69m9Ohd3OSE9K4EX4To8GIV103fJvU/A59gsqjZYu6tG9Xn9AzygStRpIYP7ZLBVtVNpLSsnj6q1XEJKaVOi49K4e536xmwWsP0KJBxX1v3BztsLOxQqeymVYdT5cKskggqH0IIS8oE8vmb0E2KX9kXvL1JvqO6qQ6/vzo9rNEXo5TPD4lIZ2dq44y8sHiEx67BDSjyz3e7CkjLZuNK4+y+OfdUDBuVpbzfqayXKygl7VaJEcH5Ezl1TusbaywdyrfDnru3i64e4sbvsBy3nxtDE/P/YtEBWUo5z4zhKbV/GHx941HzIr4fLJz9Hy9fDc/vHhfhdljZaVlWEAr1uw5o2re6D5tKsgiQWWhNlG1JIRD3jwitEZgMVFX4gnZe0HVnGthNwg7dk31Whv+3Kt6znoL5twrXL0QyxPjv2XBl5sx3f0gJkmg1YBWU+JFVHJUV5+9z4Qe91zysKD6U8fHhe++nkGrlvVKHOPoaMvrr4xirAU7iZWJLkfPuv3qEkuPhEVwPbZi49EnD1QXwuTmZM+g7rUrGfGeRITVVBrCIy+wmNBDlyyad+rAJdp0b6pqzuXQSNXrXD0bjdFosrg6RW0lJjKJ1x/7jVRzZfRuCXrZaCrimZe06ipfjHlymDojBYJKom4dV374dibnwm6wbv1Jrl5LQK834uHhyMCg1gwMal3t4+IBDp27blFi6dZjF3h8dEAFWJRHy0Y+PDK6BwuDj5gdq5Ek3n18GLbWQpoIBEoR3xaBxegyLasNnp2pUz1Hn6O3aC1DrgFtNb0J5+bo2bMxlNCj19Bl5eDgbEe3vi0IGNgabQWWiFv49RbzIj4fSQKNBHd57Ru0qEd4TKyiU/SZ0IP2/VqrNVMgqDQkSaJtGz/atvGralMsJjndskZVyWkV3+DqyYl9kCSJ39YdLnGMnY0VHzwxkr4d1Tl5BNWT8gqtEZhHCHmBxTi4WJa46GTBPFcvZzJSs1XNsXOwwcbOWvVaFY0sy6z4dQ+rfttD2l1dIjetOIpnHRdmPjOEYZO6lfvaiQlp7N+qsq6zJCFTOF5+9KOBXG7qxqaFpXds7D6iM6/99awIqxEIKhhrlbtk+dhYV3xdeUmSeHJiH4YHtGLVztNsPHiOtFuOIF9vVyYEtmdcv3a4uzhUuC0CQW1DCHmBxXTu1xKNRioaY22GrkHqE5n6je7C0m9Krx9eZM7YLtVOQMqyzLfvrmbTiqMljkmMS+Prt1aRGJfKtKfyaq/HRiYRE5kIskwdPw98G3tZtP6+LWcxqm1/Lkl5r1uViezsbRg8oStjZ/Sm2/DOrPl+I6F7wgpN8e/UmLFPDmPYIwMqdHdBIBDk0bKhj0XzWjSwbJ4lNPH15KXpA3hxWhA5uQa0Wk2lNqgSVBLlGd8uPPtmEUJeYDE+fh70GNyOQ1tCFc9p3bUJ/u3Ul3kcMaMPy+cXk5hZCqMe6q96nYpm3eKDpYr4gvz17VZysnO5cDKCUwcL185v07UxY2b2of/IDqoaHyXGK6toURqjpwXgeKsCTeDkXgRO7kXUxRtEX45FNsn4NPSiSfuG1e4hSiCoiZhMMkfOXedSZAJ6gwkPFwcCO/sX8V43b+BN+6b1CL0ao/jcbk72DOzavLxNNoskSdjZVr/dUkE5IgR4pSGEvKBMTH1uOMd3haFXUCdY0kjMeGmkRev41Pfg/meHseRrZV75oVN70bJTI4vWqiiMRhOrflNYSUeWwWhi+f/tKPbwuePhnDsezsEtZ3jpiwewtlH2VbYu4zZ6wMDWPPR80cTV+i18qd/Ct0znFggEdzCZZJZvD2Hp1hCi72rs9Nnf2xncvSX/mdgbXy/X2+9PH9qV134MVrzG5AEda0RicsIxXwAAIABJREFUqSzLxN5MJzUjGxtrLX4+btgqvOYJBLUd8U0QlIkWHRvy+o+P8Ml/FpYq5jVaDXM/n0qX/q0sXmvmK6PJzszh319Kj8sOmtCNOZ8+YPE6FcXxvRfNdk+9jcl0O5SlNPZsOIWtvTUv/Pd+Radt1LyOsvXvwt7emvEP9mHGM4NFqIxAcAtZlrl4OY64hDQ0kkSD+h40auBZZMzli7HcjE9Ha6WhQSNP6vm6l3peo8nEu79sYvOh88Ue1xtMbDwYxqGz1/nhpUk0u9XUaXC3FswY2pW/txw3a3vfDk14bFRPhZ+0asjVG9i8P4xV205x/tqdPiLODraMCmzHfUM6Ub+OWxVaKCgJkexaeQghLygzvYZ14Ms1z7P0280c2hxaJPyl24DWTJkzlPZlbMAkSRJPvH8f3Qa2Zd3C3RzZeqZQR9mOfVsw5uFAeo1QF25SWZw/rbCEpiwXqRJTGltXHWPMzD40VxCy1GtAa9w8HUlJVFi1BvDwcmLBxhewd7BVPEcgqM3o9UaCN5/i3/UhhEckFjrWvo0fE8d0oU/PZmxae5K1q44Seb3wmC49mjJhSg969ik+rOWHVftLFPEFSU7LYu7/VrPo/Zm4OeUVEZg7uT9ero78su4QmbrcInOsrTRM7N+B56YEYlWNH8pT0rN48fN/OXulaKhQelYOSzce559tJ/lgzigCu1V+eJBAUF0QQl5QLjTv0JC3f51FQnQyJ/ddIDNdh72TLe17NsO3nLshdg1qTdeg1iTFpRIbkYjJZMLHzwOf+h4AXDgXzZ5t50hOysTaxopmLeoycHh7HJ2qVojmKi2haVKZjAoE/32A5z+dYnactY0VYx4I4K/vtys+9/SnBgoRX83I1uWy++AlomOTkWWo5+NKYK8WODmKn1NFk5WVw5sfrubEqYhij4eeiyb0XDSejnakR6YW2x35xJGrnDhylfumBjDrmcGF8klSMrJZuvWEYnvikzP4d3coD4/qAeQ5PGYM68akoI5sOhzGvtPXSM/Kwd7Wms4t/BjXtx3uztW7OowuV88Ln63m3NXSS9zm6o288U0w37w2iW5tG1aSdQJFCI98pSGEvKBc8fZzZ8j9FddcpCAedVzxqHMnPvTsqQj+76stXAy7UWTsL/O3MmJcFx6bMwibKoqtdHFTePNUWQUIYP/mUEVCHuD+WYGEnYrg2D7zDb0GjO7IiMndVdsjqBiysnNZsGQf67edIfOu5j9f/7qdYUFtmD29Hy7OlpWGrW0kpmWy+tBZDp6/TqYuF0c7G3q0aMDEXu3wdnVSfT6TSWbef9eVKOILrZ2pQ2uvxSrbWOKYlUsO4eRix7SH+91+L3jfWXL1Jc8pjn92nmbmiG5oC+xE2ttaM6F/Byb076DqXNWBtTtDzYr4fIwmE5//vp2lnz0sEuwF9yTVL/5AILCAI/sv8fJTfxUr4gF02XpWLz3Mm3MXk5tjPjFXKQa9kcwMHSYFXvReg9SX3VRKZroOg8Kbv5W1lre/nU77LiUnA2s0EhNm9ualj++rlmFK9yLpGTqeeXMpy9ceLyLiAXQ6PWs2neLJ1xZzMymjCiysPhiMJr5YvZuh7/7K/OD9HLscRVhUPMcuR/HDhoMMe3cBn67cid6oTjAfP3Wdw8euKhssyxjttMhmvj6LfttLaoF+EkfOmX9IuJuYxDSi4lPND6wByLLMqm2nVM25fiOJE+fUd/8WVBByOb8EpSLu0IIaT0x0Mh+8vlKRkD11PJzvv1RXj74gmWnZXD0XxYpfdjH3gR8Y3ekdJvX8gDGd3+Xdp//i2L5LJYr6Bk196Bjgb/HapaG10qC1UvZ1NhqMfPvKMs7sPgcZWZCTCwYjGI15/8/JxZSWwcXDl8nOUN+FV1D+yLLMO5+v5eLVOLNjI6KTeO2jf9T3C6glmEwyb/y1kb92nsBQwr+B0WRiyZ6TvLJwA0YVoWz/rg9Rbsit/gtG29Lj0PV6I5uDT97+e2Z20bh2JWRmW9Zpu7pxOSKB6zeSVM/beuhCBVgjsASJvGTXcnlV9YepAQghL6jxrFl+hBydwvhzYGvwSZJuKvdYyrLM6QOX+Gj2Aia3f52nJn7Hgq+3ciE0+vYYo8HE4V3neeuJ33lj1u9kpBXfhfaR54dhbWMmwcyC7eFmbesr3lb+5YM17Fh9LG8pWUbK1SNl65CydHn/z9UjyXDu+DU+fGKhot0GQcUSej6aY6euKx5/4UocB48r9BzXMpbsCWHziYuKxu44fZm/diqLRzcaTRw8ckW1PSZr87fZA7vviFAHC7tRO9rXjvyIpNQs84OKITFFeQK/QFCbEEJeUKPJ0enZEqxuG9ZgMLFprTLPmtFo4oc3V/Dq5G/Zu+EUJgd7MNMK/eShK7zz5J/FPly07NCAt76dgW1pN2uNeiE/anovRePiIhNZ+7vCWvbAqQOXOLbLfPUMQcXy76aT5gfdxeqNKrzHtQSTSWbRbnWfe8mekyV67guSlZVb7C6HLIFJCyarvJesKRwNICv4Pqel3RGvPdqo739Rz9OF+j6u5gfWACytpGNTxh4ZgnJGhNVUGkLIC2o0URGJZKSrD/8IC41SNO6X91YT/Mct4WtvBwrjxc+djGDN3weLPdYjsBXzV81h+OTuRQS9o4sd4x/uS71GnsXOLQ7vem70H9lR0diNiw8WKtmphPV/7lM1XlD+nDitPm46JDRS9c+6pnPoQgTRieq6F8cmp7M/LNzsOBvbwknysgRGGwmjrYTJRoPJOu9ltNVgsNeQ66jBYC8hK3gutynQ5XRMv7aqRenEAR0KJbrWZJr4eaLVqv8s/g3KtzqaQFBTqB3ffME9i05FSI3aeVfPRrFmwS4AZEkCaytFTZryCV52uMQ45QZNfZj7/kT+3v06H/zyCFMe7k0rfw+cTAb2rzyEnWTCQUEpQSdXe9775ZHSPfwFOLT1jGL78zmmsHOvoOLIsiBuWm8wYjDcW2FRl2NuVtg8WxsrmjXxAcCkAaOthKwtXqVLgMYEJiuJHA8tWXVsShX0Ldvc6Yrs5mTPA0O6KLbd282R8YHtFY+v7ni4OhDUTV3PEa1Ww9igdhVkkcASyitGXmAeUX5SUKNxdrGszJ6zq/l5hTzRNtaqY9fjb6Rw5lg4HXs2LXFMXMRNvn/xb2KvFxYSCdHJIEloHOwwScU/b7ft1oS5H91HA3+fQu+bTCZO7jrH5ZBwcnP0uHm70Gt0VzzruZGRWnzsfmmYTDJZGTpcPdSX6xOUD06OtmSrfGi1sbHCSmECdG1BbRWa2/MUPvCMG9WJL77fgslGunM9KOG6IAMaQ57nXu9ijclag2O0rlhxMnpC10J/f2pSH+KS0s02hXJ3ceCbFybebgZVW7h/eBe2H1aW5wAwtFcrvNzF9anaUJ5hMULMm0UIeUGNpn5DT+o39CTqru6K5ujdv2Wpx2VZZteaAm3OLdy2jo9JKfHYlTORvDz+K7IzSqg2IcuYMrNBkugypAP2znbIMvj4ujNkUjeatva9a7jM+l938M+3m4i+XLgG8w8v/EWfcd3QWOjisHOwsWieoHzo0akJ67eHqpzT+J6rq+3t4mjRPC8XZT0eBge1Yf4fu8gymN+hyv+X1+jBaCVjtNei87LBPqHw7krn7k1o3rJeofe0Gg3vzxpBe/96LNlyguiEwqUlra00DO7ekv9M7I2vV+2IjS9IhxZ+PDs9kG8X7TY7tkVjH156aGAlWCUQVE+EkBfUaDQaid59mrFchZC3tbWiv5ma7jk6PVmFYu8tE8CaEmI9jUYTn8xeULKIL4gsc3J7KD/vfQe/pj7FDjGZTHzz9G9s+r34G5/RYGTPqsNY2Voh2zsgWSuvjNG0rR+2dkLIVyXjR3RSLeTHj+hk0Vq6HD0JSRmYTCY83ZxqVLfYoPb+2FpbkaNXHgpmY6VlUMfmisbK5CW2oiLSTAIkI8hWkOtihW1iLppbGwANG3vxxvsTi52n0UjcP7gzkwd24vDZ61yOukmu3oCnqyOBnf1xV/jwUVOZNrIbLo52zF+8m9RiyuBKEgzo3oI3Zg3FUXSern4IT3qlIYS8oMaTeCEScvRga0acyjJIEtqkVMxdZYpUTrCwJrdfCUmrx3eeI/pqvOLzmIwmNvyxl1nvTSr2+JL/ri1RxBfEkGOA3HRwd0NSmFA2emZfxXYKKoZWzerSt0cz9h25rGh8xzb16d6xsao1LoXHs3JjCFv2hpF7KydCo5Ho260Zk4Z3omv7htXew+/iYMeIri3599BZxXOGdm6Bu8LQlPOXY9FZ0FBOMsrIVhJoJPQuVtinGek/qA1zXhyBi5kwP41Golf7xvRq31j1ujWd0YHtGNKrFdsPX2DnkUukpGdja2NFqyZ1GD+wA/XruFW1iQJBlSOEvKBGk56cwf41x5EMJmQ/n7xY9ru5JeCRJKSbKeiS09i/9gQDJvcs8bxW1lrq+/sQdeWW2NYb7iS6KhQzjZvXoWX7+sUe27x4v6JzFJqz9ACPvj0e7V0PGVnp2az4ar3yE8kysk6H5Gjeo+ft586A8coT7wQVx9vPj+KVD1dx6mzpFZdaNK3DR6+NR6OijOmCZfv5bUXRKksmk8yeI5fYc+QSowe15+XZQ7CyoKJIZfLUyN4cCLtOfKr5XhFeLg7MGdVb8bktSTq+m8btffnq5Yl4ejuX+Vz3ArY2Vozs15aR/dpWtSkCFVTvR/7aRfW+IgsEZrhwIpxcnR7JYESKjIXEVLg7flWSIDMbKToeKTmvNN3p/ea7AI6c0efOKWQZcvXKRPwtwR80okOJHsyoy8q98flkpmaTmlhUnGxfsl91B1aNPtdsaUIXD0fe/302dmLbulrgYG/DV/Mm89jUPngVk3js6mLPjEk9mf/RA7iqSAL//o9dxYr4uwneHspXv25XZXNVUMfNiV/mTMLP06XUcXXdnPnp6UnU8yh9XEEcyiFXxK+hpxDxgtqPqCNfaQiPvKBGo8u8E2MumWSkpFTkpFSwt81r3CTLkJOLZChczUKJ8B08JYBFX20kMz9WXqcDK+2d8xYn0vPfN5nYteY4kx/rV8SDDljcLbW4cpYnd55TfR6T3kivga04sf9KkdKSkiTRbUBr/jNvIr6NvSyyU1Ax2Fhb8fD9vZkxqSeHQ8K5EZuCLMvU9XEhoGtTbKzVXdLPhEWzeN0xxePXbD3FqAHtaNuinvnBVUjjOh4sf2UGa46cY/neU4THJ98+1tDbjSl9OzKuZxtcHOxUnbe1f10c7G1Ue+YLlqn0qOWx7QKBoHIRQl5Qo3EoxvMoAWSXnkTq5Gr+Zurs5sCbPz/GOw/+iEFvRJJBzsgCR3uwKuGrI0l58fRGI9cvxXFoRxh9hhatb+zt607U5TizNhTEysaq2BKQWenqS0oCTHw8iOf/N5Md/xwj+loCRoMJb183BozvSr1GQsBXZ6ystPTp7l/m83zy/SbVc1ZvOVnthTyAk70t0wM7M61/J2KT08nQ5eJkZ0MdN2dVYUcFcbC3YURQW1ap6JorA3KBZ/lBPVtYtLZAUJMQNeArDyHkBdWe1JQsrl2JJydHj6ubA81b1rvd+a9Nd38cnO3uqjBjnm6DlTUP6dy/FR8teZqvnv+buMgkJFnOE/NabV5ybUFBL8tgNBWKDQxedKBYIT/wvh6E7Cm9RvTdBI7tgk0xjZ8cnC2rIe3gbI+rhxMTHg+yaL6gZhMdk8z1G0mgUtRu33+e158cZlH3zapAkiRV4TPmuH90VzbtOkumGa+8TJ5TwWR9p+a8f31POrXwKzdbBAKBoGZciQX3JBfPx/Dpe//ywPhvePnZv3nr5WU8M2shD07+jsV/7CMjXYedoy1B95WctFosWg1GrfJn2A69mrNg/7vYe7iAVoOkkZBkE1KOHiknF0lvyIvRv0vEA5w9Fl7sOfuP7YqLu7qa16MeCSz2/S6D1Hc09KjrSqM2xSfiCu4Ndu27WGq30ZLI1RvJyFJQNrWW4lfHjf++NgEH+9Lj5SXAZJVXdhLyasO/MGNAta/8IxCUmfKKjxdx8ooQQl5Q6WRl5hC87DDPTv2BKf0+YnLfD3nm/u9Zu+QQmbdi1zcFn+SZWb+xfcsZDPrC8e3xcWks/HkXz8z6jdiYFNwbeKvruursxGevr7wT+64ArVaDCcDWFuzs8l62Nnme+VLW1ucaio1rt7Gz5vmvZyIp9IaOmzWA1l2bFHts4AO9sXdWF+s7/JEBWNuIDbl7maSUTIvnFinPeo/RuW0Dfvp4GkEBzYsN05ElMNpImGw0IEnYWGv5eM4oPNwcOXo+kpOXo0nJsCwkTiAQCAoi7uSCSuXgzjC+eGPlbcGeT3pqNpfO3WDhN1sYNb0Xy5cdxkxRFaIik3jjhSXYZOSAmzOkpGN2krMTONiTo9OzfW0IY6f3Umy7k2vePDXYO9qWGIIQMKwDr//4GF888we5OSWfd/zsgcyaV3zTGAB7JzumvDiaP+atVGSTm7cLY2YPUjRWUHuxttYimQrHbyvBxckOh2JCvO41mjbw4qOXxpGQmM6uw5c4Enqds9diSUzPynORSRKO9jYM790KNw9H/m/9IS5H37w930qrYXDXFkwd1Jl2TepW3QcRCCoC4UmvNISQV8maNWt45ZVX6Nu3LwsWLKhqc2oUB3eG8f5zi5BNJX/DszJzWLb4IGiUbRZFRiRiZzDmecq9rSE9E7KL8bTbWIOTI9jdKaUYvPSwKiHfPagVm5YdUTweoOeA1qUe7ze2C627N2XT3/vYtGg/ibF5rdht7W0ImtCNUQ/1p3nHhmbXmfrKWBIiE9mwYGep46ztrOk8PoDrV2/iXsdVbPPfw/g39rZIyA/u00r83hTA29OZySO7MHlkF2RZJiI2mdQMHbbWVri62PH6zxs4vb9oV16D0cSmI+fZfPQ8L04JYuqgzmWyIyElg6s3EtEbTXi6ONCygY/FSb3VnYysHM6Hx5Gt0+PkaEubJnWxFTuM1QaJ8kt2lRDPBOYQv/kqiIyM5OOPP65qM2okmek6Pnt9RakiHgArjWIRn0+OVoMkSUhWVuDuCq7OkJMLJlNe2Iu1NRRTli/iagK6rFzsFNaGHj2tt2ohP3qG+WYzXvXcmPHyaKa/NIqsDB1GvRFHVwdVyYSSJPHs/Edo3qUJq77ZSNTFmKKDHB0wuLuwe1sYu7eF4dfYi0eeH0qfIepj7AU1E6PRxJFj14i+kYxBb8DB2ooMk0FxwqskSTwyWfnD772GJEk0qucBgN5g5IkvV3D6ajHfxQLIMnyxbBdO9jaM6a2+6dGJi1Es3h7CnlNXMBa4vjbwceO+wA5M6t8Be3Ndr2sI12OSWLzxOJv2h6ErUDbX1cmOMf3bcf+wzvh4iBr9gnsLIeQVEhcXx8MPP0xKSkpVm1Ij2bY2hOxM8wlysgWxt7IkIWk1kH8T02jAXlnMeE6OXrGQ92/jS9DoTuwKPqlofM+BrWnTpZGisZAnAhwtrECTP3/kowMY8UgQp3af47dPg7l4OjLv38PRHklb+N82OvwmH85dzFNvjWHMNCHOajO5uQaWrTzC2uAQbhZoKmbSSmitJIzWJfRFKIgs88Corni4qUvSLg6j0US2To+dnXW5dYpNScviRGgE6Rk52NtZ0761H/V8XMvl3Jaw6ch5Tl0pXcQX5OuVexnavSW2KnoB/Ln5GN+s2lvsscj4FP63Yg8bD59n/rMTanz9+v0nr/LG/OBCAj6f1Awdf284xvp95/jfSxNo3aROFVgoKIRwo1caQsgrICQkhLlz5xIXp67ut+AOm/5R1nRGtnTL3oJpWisNjk7qkkSf/3Qymek6ju4uvXRkxwB/Xv1qWpWEIEiSxNXwZC5FpCK5mS+7938fraNRszp06NG0EqwTVDbZulzeeHsVJ09HFDkmGWWQQIuE0Uou2TMvy/Tv2oynHw6y2A6j0cSRk+Gs3nySwyHXbnuP27X0ZcKwTgT1amFReMT1qET+WnWYHfsvoC+QGC9JENClKdMn9qBjFVRoWrHrtKrxKRnZbD9+iZEBpYfj5bN6b2iJIr4g5yPiee67f/nl5SmqHhKqE2evxPDqN+vQ39XY726S07J47vN/WPjeNHy9q+4hTiCoTGrmt7qS0Ol0/Prrr/z444/o9Xo0Go3FHTnvdW5EJioaJyFb9CBvZaXBaCz9In83AUGtsbJWtwNgY2vNuz8+RPDiQwQvOkDU1YRCx+s19GTUtADGzuxTZVVhDHojqxaav8HnI8uw8re9QsjXUj75bH2xIh5uPf8aZJBBawI0YNJyuyylJIOtVsuD9wXw4AO9VD+YJqZlsvHwecJvJHEg5CqJCRlY5ciF4mfPXLjBmQs3+H3lQT5/YyL167krPv+J0Ahe/+TfYjutyjIcPH6Vwyeu8fJTQxk9uL0q28tCbFI6Z8NjVc/bplDI63INfKtAxOdzNjyOjYfPM75vzQyj+27ZXrMiPp+U9Gx+X3uYNx4bWsFWCUpELseGUMKzbxYh5Evg+vXrPPTQQ8TE5G2NOjk58eWXX/LEE09UsWU1E7Ox8fkYTaAmnPNWlZoZj/Xnj+935L2nUGyMmaqy/vwttFZaxj3Yh7Eze3PuxHViIxORZajj507bbo3RqIzxL28O7QwjKSFd1Zxjey8QG5VE3foeFWSVoCq4eCmWvfsvljpGIs8zLxsBjUyLJnWxttbi7GRHr+5NGTqgLY4OtqWe424S0zL5avketh2/iKFg+VVHDQYHGW2ujHVmYUEfeSOZZ95Zxs+fTsfb03yc8/WoxBJFfEFMssxnP2zGy92RLh0acuh0ODE305Akifp13OjRrlG5hffkk5yeZdG8lAxl87Yeu0Caylr+y3eeYlyftjUuUflqdCInwqJUzdl04DzPPNAfZ0d1O66CckQI8EpDCPkSiI2NvS3ig4KCmDdvHvXqVf+25NWRuBvJgFSywC5QMlLKNSLbKojXvT1BgqQ04sIiCBzRgd2bQvPOZ2Z+r4Gt6dizbB5oSZJo27Uxbbs2LtN5ypuwk8V7X0tDluH86UjFQt5oNHHs4GWCVx3j7MkIdNl6nFzs6NGnBWPu60bLttWre2V2ho7Y8ASMegMeKjy+NZ01wSGKx0oAJvBxdeTj9++zeM245HRmfb6c6JtpJY4x2mowWcnYpJnQFND5CUkZfPfHbt57YbTZdf5eddisiM/HJMOHP2/GqJVITisslut4OjNxUEemjeyKdTnVx7c0hMVG4bydIZdVn/tCZDwxiWn4etWskJM9x9V/1pxcA4dCrzMkoGUFWCQQVC+EkC8BSZLo0qULc+bMoU+fPlVtTqnERiWxa/1pEuNT0Wg0NPD3ZsCoTjiqbBJUEcTHpPDCzJ/JzTWULLDz35PlPDGRowc7G0WCHFlGSkhmx7IDLLr4NU4u9qxfXnplme79WvDqZ1NqnGdKKTqF4uZucrKV1chPiEtj3otLuHyhcOhAanIWW4NPsjX4JH0Htubl9yZgV8X1xq+evs6aHzazY8l+cgp4MHu/2aoKrao8jh67pnrOsRPhyLJs0ffDaDLx/HdrShXx+d9pWSuR66zBNrVwR+Rdhy6SmJyJZymdj1PSsti+/4Iim2TAaCdxM7P4Bkxxien83/J9HD1znS9eHI9dOVR48fN2xdnBlnSVXvNWDX0UjbO0mVRqpq7GCfmUdAs/q2i4VaWUW2iNwCxCyJdAt27dWLJkSVWbUSrR4Tf5+b/rObrnQpE+SL9+tpEhE7rw6AvDsXdUty1ennz62nIS88M8FAoDKSEF2c0JnM1UWZBlpMg4pOwccoCjm08z552x9AhqydpFhzi+/1Kh4c3b+jL6gZ4MHtsZbS3uTOniZll1CmdX8/NSkjJ5+YnfiYlOLnXcvh1hZGXm8MHX06qsC+i6H7fw3dyFZsO6dFk52KkMHakpZKoUkgB6vRG93oiNBTkeB86EcyEywfzAW8hWEiZr0BZ4hjQaTWzbF8b9Y7qVOO9EaEShxNYSz0+eiJe1xXRfBUxWIN+Kqjl8MZL3ftzIJ3PHKra/JGytrRjTqw2LtyvfEQGY1L+D4vNbQk1MdrW0PryoKy+4VxC/6SVQ1XHO5rh6PobXH11AWkrxMZU5Oj3BSw5zITSKTxY8ViXe+YtnozkXoiLMQ5LyxLnRBFejkX29wNO1+LryOblI0QlIaXfazCfHpyJJEj0DW9EzsBVxN5K5EZGE0WDCu64rjZop83bVdAIGtGbpT7tUzbGzt6FjgPlQo5+/3mJWxOdz4vBV1q04yoSpAapsKQ+2/b2H+c/8pmjspw9+xzvLn6/233lLcLC3IStL3Q6NVqvBWmUSeD4rd6ur1AJgsNOg1RcuIhB3s/Qcj/QMZQ8osjbP819wd08GjLZgsKOIwN948TI+/+xhztg+ZQ6zmTKgEyt3nyZXYZJmUCd/Gvi4KRrbpnFdjpyPVGWPi4MtfjWwkkubppZ1vW0juuVWLcIjX2nUujvXzp07admyperXwIEDq9p0xWRm6HjnyT9KFPEFuXQmmi9eW1EJVhVl46qj6idJEt37tUCSZTTRCUhnryJFx0NSGqSkw80UpCtRSGHhhUQ8gPVdW+J1fN3pHOBPt77N7xkRD9CifX2aq4xRHzi2k9lSnClJmezeekbVedetPIpJaaJzOaHLyuGHF/5QPP7AmqMc2ajOc1pT6NxJeR+D23M6NrQ47Ozk5Ruq55iKcSeZ60hqrzBky2SVX37njojPdQK9Ywleeq3E77uO8/SPq8nKURZqVhINfNz48LERirqrNvPz4t2HlFdZmdi/veI0onzG9mlXIz3yfTo1xcfDSdWcji388G/gVUEWCcwil/NLUCq1TsjfC2xfE0JiXCkxqHdxaGcY4ZfUl0IrK9evxFs0r+fQO2XiJKMJKSEFTUQsmvAYNFHxSOlZxZaNb9q+gYWW1i4kSeKR54ehKUaoFIewwOzFAAAgAElEQVSLuwNTHg80O27HptMYDOrKr0ZHJHH2lPrk27Kwa9kBMpIzzQ8swLoft1aQNVXLuNGd1c8Z08Xi9bItEb/F/JrWr1u6Z7p9az/z6TPkeeQLoncEk41EkVjEuzh8MZI3/tyIXMy48KhEFq85yk+L9/LHqkOEnI0sdhzAoK7N+faZCSV62jWSxNBuLfj15cm4qKiw4uflyuCuLRSPt7W2YsqAjorHVyestBqmjSg5zKo4ZoxSN14gqMnUvMdzM/j7+/Piiy+qnufkpO6Jv6qQZZngJYdUz1u/9DBPvz2uAiwqGZNK0ZdP64AWePm6c/OGshAOgEat/WjbS/mNrbbTuXczXvpkMl++vhKjseSfg4u7A+//+DB1/MxXcom9YVlX49joZNp3Vu8ZtpRdyw+onnNs8ykyUjJxKoeupdWJNq196dalMcdOhCsa37xZHXr19Ld4PRdHW5LVJifepYHtbK0Y3Lf0Wur1fFzp1bUpB45dLf3cBdS+SQtG28JhNqWxM/QKp67F0KmpLwCh56P5eek+TpwpGtLSuL4HD04MYFj/NkWO9WrbiH/ef5gj5yPYfOQCiWmZ2Fhb0dzPi3F921HXw3ypzeJ4a+ZgohNSOXe99EaFVloNn84eiZ/CJFdZlgm5Es2xy1Fk5ehxsrOld+tGtGlYdd1SHxjWmUsRCazfe9bs2NmTetO/i+W/w4LyQSS7Vh61Tsg3bNiQ2bNnV7UZFUZGajaRV5Unk+Vz9sT1CrCmdDy8LbtBedV1Y/xTQ/n1rWWK50x8ZnitrUJjKQNGd6J+E29WLdzL/i1nCnnTHZxsGTK+CxMf7oePr7K43JK8jtWNlLhU1XNkWSYlPrXWCXlJknjnjXG89PoyLprZlWtQ34OP35+Etgw11fu2b8K6A+dUzdHmFv69Gh7UFicFCfrTJ/Tg0PFrmEr7vSwg2g35p1RxnVi27xSdmvqy69BF5n0djL4E50R4VBLvf7uB8KhEnpjWr8hxjUYioE0jAtqU3wOtk70tP714H58v3cWGw2GF6/Xfwt/Xk1enDqRrS2WdbbeEXOTnjYe4HFO4gd93wftp16guT43qRe/WjcvDfFVIksRbjw+lQR03Fm04Vmw1IG93R2ZP7M3YoMpr/CUQVAdqnZCv7VhaWlCnMuGtPBgwsiMHdoSpmtO1d3PcPByZ+OwIzh+7yr5/zcfZD384kGEP9rfUzFpN87Z+vPbFAyTfTOdCaBS6rFycXe1p26Uxdg42qs5VT4HXvjjq+lZu3XYrC6tV3J1jUVtwdrbj68+nsvDPfWzYfJrMzMIiyM7WiiGD2vHYI/1xdbG3eJ3sXD0+Cho53eaWyLbKuSPEmzX25skZyr7LHdrU55WnhvLZD1uKFfMSIJnuhNcY1f26A7DnzFUuXo0rVcQX5M9/DuNbx40xgypHTDrY2fDuw0OZM7EPa/ef5XJ0Irl6A56ujgzt1oLOzf0UOzh+23KEb9ftL/H4meuxPP1/q3ln6hAm9Kr8DrEajcQj43oydXgXth6+QEhY3o6Bs4MtvTo2pn9n/yqrkCUohprh96kVCCFfw3C08Ebr5Gr5DdpSeg1ojaePM4nxyruMjr6/B5BXOeONP57i17eWse7nbehzDEXG2jrYMPm5kUx/fbzwxpvB3cuZgAHmW7+XxoBh7Vkwf5uqOHnfBh607dSwTOuqpUn7hlw8bibk4i5cPJ3wrOQHjsrE3t6Gp54YyCMP9WXvvotE30jO60Zcx4XAfq0UecBLQpZlFu0O4afNh0jLykGjAY0p7z5e4rfylojX5MhIt77aXds35IMXx5jtIpuTa2Dv/otcu34Tvd7I6KC2hF6O4VpkYpGxno723NTp8v5iwSUiQ5fLX6sPKxLx+fyx6iAjg9qWaWdDLZ4ujjwyoofF87eGXCxVxOcjy/DBkm3U93Sle4uqyUmys7VmTP92jOlf+Q8TAmVIgFROO7jizm4eIeRrGA6OtrTr1pgzx8JVzesZWPkNcKystTz79jjmzV1ktpY3QN/BbekZeKcTn9ZKyxOfTuOBl8aw5a89nNoTRlaGDkcXe7oOas+Q6X1xVFD7XFA+uHs60X9wW3ZsClU8Z8x93RVV7ShPRs8ewubfd6maM/yRAVjVwIoearG3s2Ho4PIVQP9bu5c/dhy//XeTDZBLoY6tRZAkbEwSvvYOdO7WgAnDOtOmed1SH8j1eiN/LjnAmvUnSUsrHIevkSQ6tfWjib83GisNdrbWdGpTny4dGvH4vMVcvJ5g5smieGystOw5oq6zaEx8GodCrtGnm/I4bVmWCbsSy79bT3M5IgGDwYSHmwODe7dicO+W5dKkqrS1f950WPF4kyyzYOuRKhPyAoGgMLX/zlULGTM1QJWQ12glhk/uXnEGlULPwFa8/tn9fP7GCvS5JddT7je0HS9/NKnYWt6uXs5Mfn4Uk58fVeSYLMvk5hiwstJUuyZPmRk5bN90mj3bzpGanIW1jRb/FnUZPakrLduoKw9ZXXji+WGEhUYpqiXfuUdTxlTB713L7v607d2SsweUdf60trVi9BNDKtiq2smmExcKiXgApDwxLxtBYyia9NbQx43JAzoyObCj4lrtubkG3nzvH46WkLRrkmVCz0QRfv0mX3w0hZbN79QQ/+LF8Tz10QquZKbkPWSooLGnGzci1FffOnE2QrGQj7uZxjvfrCf0wl2lO6/DkVPXmf/nLp57ZCAjikmkLQ9OXr3BpRs3Vc05dD6C6/HJNPKpvbtYgjJQnmUjRYiOWYSQr4H0HtyWlh0acOG0soYg42f2wdPHpYKtKpn+Q9vRoq0fG1YcYdPq46Ql59W/lyTo0b8Vo6f0oFvf5qrCY8IvxhL89wF2rQshMz1v67yBvw8j7u/J4EndFHUprUjWrTzGr99tI/uu3ITLF2LZvO4k7To15M2PJuFpYUJwVeHm4cjnPz3Muy8s4crFkpMn+wS14pX3J1jcWKisvLHoWZ7r/w4JxYRb3M2rv8+hbuN7p89AefLn3SI+HwlkKzBqAdMdMe/mbMfSd2eqrmf+zf9tK1HEFyQ9Xcdr76zk958eux3v7+PhzK/zpvLOrxvZfU1d0n+3Rn6sDVEv5LOylZXhjE9M5z9vLyUuseTww/TMHD74biM6nZ4JQ8u/hOSxS1EWzxNCXiCoeoSQr4FYWWuZ9/1M3pr9O1fCSm/AMnh8Fx59cXglWVYydf3cefS5YTz49GCSEzPQ6w24ujuabUJ0NyaTiQWfruef3/YUORZ5JZ6fP17HovlbeWP+TLr0rZpylMv+2M+C77eXOubMyQien7WQr399FA+vmlH6NB/vOi7M/3MWRw9cZv2qY5w5eR1dth4nZzt69m3B6MndadnGt0rzFrzre/LN3g/478Pfc2pX6SXr+t9X+Z1nawPnIuM4G1l66UMkQHvHqZacrWPvuWsM7thc8TrxCels3KI8nCs5JYv1m04zbUrP2++5Odvz9dwJTP18EeejlVX98q/nSXf/+qxFfbMwB3trjl6MZHfoVVKzdNjbWNOxaT0Gd2pe6CHmvfkbShXxBflywXY6tPLFv6G3antKIyvHskIIZW2YJajdiPKTlYcQ8jUUN08nPv9zFv/8sY+Ny4+SGF+4QZR/a1/GzezN4HGdq1UiqJW1Fu+6lrcJ/+nDtaz9s/SkrMx0He/O+o2Pfp9FhzLUxLaEsNAosyI+n9gbKXzxwRo+/mZ6BVtV/mi1GgL6tSCgX/Wt3e/l58HnW9/m6unrbPh1O9fPRaHPNeDp644R9SUqBYUJi7Ss4dv5qARVQj5440nV3YHXbjjJA/f1KJSfodFIzP/PeB79ZjmRN0v/+ddzd2b+7HE42dhgba1Fry85LPBuDLaw4dIVFhw9Wej95XtP8fmq3cwc0IVHhnTn0vV4Qs4p94abZJlVm07yyuzyDQNzsrcs0dm5lKTkbF0uJ05FkJaWja2tFa2a18O3nrIytwKBQB1CyNdg7B1tmf7UIO6fFUTo0WvcjE9Dq9XQoKk3zarYI1oRhB65albE52PQG/nylWX8tuO1Sq0esXqZ8qQxgGMHrxB5/SYNGol24hVF0w6NmPPto4XemzdvXtUYU4vIMRStJKWEXJXzTp1RH/oRG5dKXEIa9eoUdhr4uDrx5/MP8MXq3WwOuVik9rpWo2FQx2a8PDEQH9e8nbLBfVqx0cyuTj56e4lcVw0xKcV72VMyspm/bj/X4pJwyVEfdrZ5bxjPPBiEvV35Jb/2ad2Y+Qoq1hREI0kEtCxajSrhZjpLVh1h07ZQMu8KK+zRpQlT7+tBl46V1xxOUIUIj3ylIYS8Si5cUJZAV5lYWWvp3LtZVZtR4QT/ra5jZ3x0Mkd3hhEwuG0FWVSYtJQs9qmsmw+wcU0Is58tHy9bjk7P7u3nOB1yneysXBydbOneqxm9+rUQNZYF5YqHk2V5KO5O6krhZussC/24Oz8lHw9nBz5+cAQvTghk/dEwohJTQQZfTxdGdmt1W8DnM2N8D3YevIjOTCiJ0RpyXZQ5T4KPhNHQQX1+THaOntibaTSp76l6bkm0auBDh8b1OB0eo3hO/3ZNqete2P4r4Qm8/PZyEpMyi51z5MQ1jpy4xtOPD2DKhKopviCoPERoTeUhhLygRpCVoWO/ijjZfLatPlZpQv5GdHLhGusGI1JGFlJmNphMedm91taYnB3A3vZ2h8mIa+o79d6NySSz7M/9rFh8kIxbyb/5bFp3Ek8vZx6cFciIsZ3LvJZAAHmeXHsba7JzlcdKSxKqwmoAnMzUlS+JhMR0Vq8/wdnzN8jJMeDiYk/fns0YNawDbq4OeDo78ODArmbP07i+J5++Op7X/vtvqWJe76hR1TX2RnYG1qivk20wKA/zUcqTo3rx9A+rS++SewsbKy2zh/cs9F5iUgavvL2iRBFfkO9/3YmHuyODgyqmCo9AcK9ReTEHAkEZSEpIx6iiKUs+8dEpFWBN8Rjzb7CyjJSSjiY6Hk1qBpLBiGSSkYwmJF0O2oRkNDcS4JYAUtNgqThkWeZ/H69j4U87i4j4fBJvpvO/T4L5e0HRJGGBwBKc7W0Z1U1df4rerRrTwEtdrHSPbk1VjQewt7fmlXdWsnbjKa5cSyDqRjLnzt/g5z/2cN+D/8fvi/cjq2hY071DI37+eBoDAloU2xehZYu6mOzVSXKDLGO04BnF081R/SQz9GrViHemDTHb88HGSstnj46iTcM6hd5ftfY4N5MyFK/3yx97MBrLdt0TVGPkcn4JSkV45AU1AkvD/aVKbEbk4ZW31SylpKNJyyz1+iMZjGjiEjHV9cLTq2wlKP9dcZTN608pGvvnr7tp2rwOvfu3ND9YIDDDrKE92RV6hZvpWWbHOtha89yYvqrXGDG0PQv+3Ksq4TRLpy/R1a03GFm4aD9p6TqemT1QcS6RfyNvPnxpLAmJ6Rw+GU5ahg57O2vaNK/HqahYTizfodi+fEzWEuSoeKBo3xCPChDyAOMD2tLAy5UFW45wIKxwmU6NJBHU3p/Hh/UoIuJz9QaCN59WtVZsfBqHjl2lT8/aHxIqEFQ0QsgLagSedVyxsbMmV6eu5Jlvo/KLJTVHPT93GtVzIep6jKImkpJJRnMzhYHDLe+0aTSaWLn4oKo5KxcfFEJeUC7UdXfmx6cm8dSPq4lPLdkj62RnwzezxtHCT33pRFcXe+6f1IO/lyr7PVcqi1etPU7nDg3p10tdqI+3pzOjB7UnLimdVTtO8ePXq7mZlQV26p0GtnZWyBnKr2kThnZSvYYaujarT9dm9YlISOHE5Sgyc3JxsrOlZ8uGRWLi8wm7EEPqXZ12lXDwyBUh5GsxIka+8hBCXlDupCamk5Gcia2DLR51XYvt1qoWO3sbgkZ3YsvKo6rmDZvS0/ygcsTBmFeRQ8ktXQakXD0OVub/fUwmEyGHrrBp1TGiwm9iNJrwruuKZz03EmJS8laUULR1ceZUJNcux9GkWR2zYwUCczT39WLZK9NZvu80Kw+cJiH1Tpy0q4MdEwLa8kC/TtTzsLwp3aMz+5KUnMGGzQryZCQUB56vWntctZAHOBgazmvfrSP7Vsy8ZGEeeUD7xhzedVlRbHq/bv707145wrehtxsNvZWFQGVkFB/OZ460EsIABbUEIeQrDSHkBeVCbo6ePf8cJfjXHZw/euX2+3UbezPykUCGPxiIi2fZGh+NmdFblZBv2KwOHQMqr458WlIGV0KuKR6frzW2LD5A665NShx37WIsn7y8lIireUmxMoBG4npkEmg0t84j3/qvDBrzov5CWIwQ8oJyw8PJgf8MD+DxIT24HHOT9OwcHGytaVbPS3UX1+LQaCRenjuc9m3rs3L1Ma7clSDu5GhLk8ZenD4XrSp7NOR0BFHRSdT381A859SlaF76Zg36AkmnkhGQZdUxgNOHdGFkx5a8//3GUkOH+ndvxntzR5qNYa8K7OxsLJpnb19+JTQFgnsZIeQFZSY5PpV3Jn/NpZDwIsdiwxP47d2VrJq/mfeXP0dLCxLX8mnWrj4PvfD/7d15WJRV+wfw7zML+yoIgggKioDgvuG+7/taLmm5m2Vlr2lZWZZlZfn+9K20t9xeMy0Xcq3cMzVccN83FEVQkH2ZgXl+fxAIwqzOCt/Pdc3VMM8589wkM3PPec65Ty+s/nK31raOzvaYs2SUWWvpJyekGrQgN/FW2Y11RFHEo6R0ZGXk4VFSOj6dvQHZWflFxwBALgWk0uLGJcmDCBECBEAlQhRQtJRdze+v4K6MZAIyqQRhAT4meW5BENC7exR6dYvEpSuJuBX/CAXKQlTzdEaLZnXw1Td/4Oyle3o/7x09EnlRFPHZmn1lknig6LuDRAmo9Mhp6/p5oUlITQh1BYTXrYEtv5/Gtn3nkfHPCLcgAK0b18GQHo0Q3STYKpN4AAgN8YWdnRQKhX7VdKIiapooIrI0AcabWmOdf/XWhYk8PZOczFy8PWgxbp2/q7Fd+qNMvD3oC3y55x0EhRn+Bj5yWhfY2cuw8vNdasuw+fh74N1vxqNOmL/B5zGEPlUwyvYr+m9uTj72/Hoa2zf8jfgbyU8OlCb7J4kvTuBLJepCqbF5QQRElQhIK34bdHXXr5Y3kTmJoojjl+/i3M1E5CsK4O7iiA6NglHLxwOCICAizB8RT72+Da2Cok/VqLPX7+Pa3YrLxUqVgEr2zxUxLSSCgJmD2pcMNPj7uOPlMR0x5bl2eJSWDYWiAJ7uTnB1dtA5NktxdXVAlw7h2L3nvM59nJ3t0bVjuAmjIqo6mMjTM9m87DetSXyx7IxcLH9rPRbGvGnw+QRBwJAJHdF5YFP8tjEWB7bFIfVhJmQyCYJCa6DPc60R3T0SMrn5Nz/y9vOEIAh6J/TVAzzxICEV70xbg3vxj9S2EwUUJfJaLuELKJXMV9DW3l6G5q3MN+WISFeiKOLXvy5g9e4TiE96XObYlxsPIrpBEF4e3BbhQeWnhXlXM2zqXnUv3atG/X5M/YaAggjI8oACB83JvEQi4P1R3dG+QfnpdDKZFDW8DV9LYCkjBrfA3oOXdK4sNHxgMzgaOCWHbICIigeiDH0u0oiJPBmsQFmAXasO6tXn1P4LSLj2AAH1alR4PDU5AykP0iGRCvANqAYX94p3j/T0dsVz07viueld9Y7bVDx93NCsSwRO7NVtO/di0X2aYM6klXhw77HmhsXTaXSYLlTSooJR+c49IuHqxhF5si6iKOLLjQfx4544tW2OXojHqav38MX0/mgTWbvMse6dI7B+U6xe56xV0xP11bwXVSQlXfOGRxIVIM8FCuUiVHKUea1KJQI6N6qLcV2bIaq2n15xWruQ2tXx7r/64YNF27ReGenSIQwvPNfGTJGRpbBqjfkwkSeDnfvrKlIS9d9w6cAvxzBm7qCSn0VRxN9/XMC2NX/i1KEnI14yuRTt+zXGgPHtEdaktjFCNrn+L3bUK5GvFVoD584maE/iAUBqQPWfp95M3Tyc8Px4/Wt5E5naT/tOa0zii+UrCzD7m+1YO28U6vg9mdseUscHUQ0CcO5Cgs7nHNiniV5zz2U6VJgSRECmAEQFIEqL1qtMGRSNwe2jUN392Rb8W7OObetj8UeO+HblAVy++qDccQ93Jwwb2Ayjh7e22vn+RLaIiTwZLPWBYbumppTqp1QU4MtZP+JAzKly7QqUhdi/5ST2bzmJcbP74rkZ3Q2O1VxadItE9+ei8YcONa/tHOSYsWgUPnhzg8niEfBkeo27hxM+/vJ5+Pl7mux8RIZQFhRi5U7dR9NzFUr874+TePeFsu8Jr0/rhhn/+hE5uQqtzxEZXhMD+uhXl71uQHX8DvXTa0oTUFTNRiIIGNQ2slIn8cWaNAzE8q9ewKWriTh89BoyMnNhbydDWKgfOrQNhZ0RKhiRjeCIvNnwVUUGk8oMm4cu+6efKIpY8q+fKkzin7b6sx1wcLTDoAkdDTqnuQiCgJmLR0NuJ8PONX+qbefm6Yx3V01BWpYC2QbWYdaFCMDFzRG9BzTB4BEt4e1je/NvqfLbH3cdKRnad4ctbdexy3htWHu4Oj1ZEBpSxwefLxiOtz/crHGTokaRAfh43hDY2+n3EdivXQMs33wEhSrdF8i2axwMHzWbKVVW4aF+CA8tO30oMzsPdxKLrjz6VHOFm4v1L+QlsgVM5MlgQeGGVYUJ/KdqzYXYm9i35YTO/VZ+ug1dhjSHm6dptig3FqlMilc+H4XeY9thx6pDOBhzErn/JOu1w/zRZ1x7dBneCs6ujtj209+6P7GGKjTqCCoV1sW8CkdHe736EZnT8cu6LZgvLV9ZgHM3H5SbKx8ZXhNrl0/Erj/OIWbnadwvdQWwWaMgDOrbBG1a14XMgKlq3h7O6NG6PnYduaRzn5Hdm+h9nsrk9OUE/PJ7HA4cv14yf14qlaBDs7oY1rMxmobXsnCEZHQiIBhWRKrC5yLNmMiTweo0qIWwFiFlNoDSxt7JDl1GtAYAbF97WK/zKfIL8MfPsRg6ubNe/SylbsNAzPxyDF5dPBqKPCVkcmm5qxh6VdcpLNR7nrygKACM9YZKZCLZedqnwlQkJ7/ifu5ujnhuaEuMHNIC6Rm5yMtXwsFBDhdnB4MS+NL+NbYLrt99pLYMZWlTBrdBi4jAZzqfrRJFEUt/PIj1O06WO1ZYqML+2KvYH3sVI3s3xaujO3HePJGBmMjTMxk0rTs+1SOR7zG6HZzdnVCgLMRfu87qfb6Dv56ymUS+mCAIsHesuNRabU27qwpC2RJeKrHopusHnkqERATsuYMiWTkXA68Yaet340EKNv51FrtOXUZGTtGmarV9PDE0OgoDWzaAuwF12l0c7fHtnOH48PvfcPBUxe99zo52mDa0LUZ0q7qj8St+/qvCJP5pG3adgp1chunPtTdDVGQ2HEk3Gyby9Ew6Dm2J80euYvt/92ltG9Y8GBMWjAAAZGXkokDHmsOlpT3K1LuPNQtrGIA6oTVwq4IqDwDKJPMCAFGpBOzkTx5/uhRl8WOiCCFPgaZt6kIiebYRSCJTax0RhE0H9fti7+Rgh6jgiss4qlQi/r39MFbtKz9173byYyyOOYRvfzuGz8f1Rdvw2nrH6+rsgM9fHYj4B4+xZf9ZXLz1APnKArg7O6Bz83ro2ToMTlW4Tvq9pDSsjtF92uDaX2PRv2MkavlxIX5lwfKT5sNEnp6JIAh4efEYuHu7YuNXO6DML6iwXbtBzTHr6wlwcCoaQZMbuGGTXM/FadZOEAQMHhONL9/boqnRk2ReJULMVwJyWcXTbAQBKFRByFNCEEX0GxVtosiJjKdDo2D4eLggOS1L5z79osPhrCZZ/vLXQ1h7QPMi+uw8BV79bwy+njIYrUINm/4SVMMTrz1v3QvwLWHrvrN67we0Zd8ZvDq6k0niIarMKldWRBYhCALGvj0IA6Z0xe//O4xjO+OQlZYDByd7RLSui74vdS63AZSTqwN8a1VD0t1Uvc5VJ7ymMUO3Ct0HNsWFuDv4bYuGy9D/jLx7uDsiLSUbKFBAFARALi06JgBQqSDkF0AoVAEQ4eTqiLvXkxDRONDqFwhT1SaTSjBlQDQWrPlDp/aujvYY3b1phcfO3LqvNYkvVlCowrs//oad70545rnz9MSeo5f17vPHkStM5CsTY+3sagHdunXD3bvaF+DXrFkT+/Zpn41ganznIqNx93LF8Jm9sfi3t7H874/w7/3vYsonz1e4i6sgCOj9vP6jxX3HVL4dAQVBwMz3B+L5yZ1gZ1/xd2uJVECvIc3w3Y430LhVMAQAElGERFEASZ4Ckuw8SLLzIRQUFr2BikBORi5+WLQDY9t9hFVf7IJKj5J5RBVRqUTk5imhUhn/Q3pQ+0hM7t9aaztnBzt8NWMAAqp7VHj8p8Nn9DpvUloWDpzXfZ0PaZeSpl8pUQB4nJ5dtOcFkQVlZWUhIUH3TeWsAUfkyWJ6PtcaG/6zB7nZ+Tq1rxPuj0Zt65k4KsuQSCQYN6MbBo9pgz9iTuH44avIzsyDg5MdGjarg15Dm6N6DXcAwIIVL+KXH/7EjvXHkJKUDhSqNI5+KPILsOHbfXiUlI5Zn42E8PS8eqpSCgtVuHLtAVJTsyGXS1AnqDp8NOwvUFCowl9xN7FpzxmcvHgHhSoRUomAZhGBGNqtEdo2CTbaaPaUAdGoW9Mbq3cfx4XbSWWOSSUSdG1WF5P7t0YdP68K++fkK/HH6at6n/fX2Ivo1qhyvrdYglwmgbJAvzVQMpmU702VhCAab468uefaX758ueQL5QcffIBGjRqpbSuXW0chCSbyZDEe3q54Z/mLmP/id1oXvnp4u2Le8pcq/cJNNw8nDB3XDkPHtVPbRm4nw/NTO2P4hA74v3c34Y9fjuv03Hu3nER4kyD05bz5Kik3V4Gt2+KwbUccEh+klzwuCECrFiEYNqQ5mjWpXVY/KWYAACAASURBVKZPSlo23ly8FZdulU2qC1UiYs/HI/Z8PMLr+OLzNwbC29M4O5d2bVYPXZvVw6X4JJy7mYg8RdEi0rZRdeDtrnmKWGpmNpSF+l95SnycYWi4VIGQwOo4d/W+fn1qeZsoGrIIG724cunSkz0iunXrBm9v6/+7rNxZEVm9Zh3CsPDH6fALUv9iiWhWB19tfQ3+ta3/BWVOggDEHdZv9HHrqj95+boKSk3Nwquz1mHF9wfKJPFA0cWcY7E38OacDViz7q+SxzOy8zDjk1/KJfFPu3QrCTM++QUZ2cbdoTg8yBcjOjfGCz2bY2C7SK1JPIDyVZx0xJFg4xrUpaHefQZ3VT/ySWQuxYl89erVbSKJBzgiT1YgqlUI/nvwbZw6dAV7N5/Ao8Q0SKUS1Ayujl7PRaNeQ+78V5HjBy7j0VNJmTYJNx/i3N830bB1iImiImujUBRgzru/4PqNZK1tV645DDc3Rwzq3xTfbz6KW/dSdDrH7fup+O+mo3jjBcvu8VDdzRkOdjLkKSqunqVOoHfF8+3JMF1b18c3G/7Eo8fZOrWv5u6EbtH1TRwVmZOtlp8sTuTDw8MtHInumMiTVZBIJGjeKRzNO9nOi8fSbl1JNLgfE/mq44+9F3DtuuZR9dL+u/IQOrQPxfZDF/Q6z44/L2DqiLZmr58uiiLuJKUhLSMHdnIZejYORUzsRb2eY3DrSBNFVzXZ28mw6I1BmPHxRuTmKTW2dbCXY9EbA+Fgbx3zjanqKigowPXr1wEAERERFo5Gd0zkiWyU0oANtQBAqedoJdkuURQRsz1Orz7Z2flYsf4vZOcq9OuXq8DBE9fRu515PgDzFEps//MiNu07jRsJT64c2DnIAEfdnyeouiei6weZIMKqLSKkBr55byQ+/HoXbiZUfGWnds1qeH9aH4QFa9jhmmyQaMTyk+Yb2r9+/ToUiqL3veDgYKxbtw47d+7ElStXkJ+fjxo1aqBNmzaYMGECAgMN23vCFJjIE9koTy/DFhd6ersaORKyVskPM/UajS929qJh5deSUsyz83JKejZeX7wFl+PLTxdS5BVAKgKFTtrnvTvZy7FoXB9IJJwjbwr1a/vif4vGIe5SArYdOI/7yWkAAH8fd/TrGImmEbW4PqEyMmLVGnMumr18+cn+Bx9++CGysspuUHfnzh3cuXMHmzZtwvvvv4/hw4ebLzgNmMgT2ai2PaOw/ONfUVige5UOe0c5WnW1nUuG9GwyMnIN6pevZodmbSRmSMry8pVqk/hisnwAEFHo+M9maRXw9XDBVy/1R3iAj0nipCKCIKBpRC00jeBaJ7JuFy8+mZKXlZWFzp07Y+DAgfD390dqair27NmDrVu3QqlUYt68eXByckLfvn0tGHERJvJENqqajxva9ozCoR26b4DTuX8TuLjpMe+AbJq9nWFv8S4OdkCObgsVSwuoYfpFozEHz2lM4ovJ8gGpQoRLNQd4+rjgYUYW5FIpgmt4YVh0FLo2rAu5TGryeImqJDMvdt2/fz+mTp2qd7/Su7MWL3QVBAEff/wxhg4dWqZt586d0adPH0yZMgVKpRLvv/8+2rdvDzc39ftwmAMTeSIbNn5Wb5w+ch0ZOlSH8PJ1x+hXe5ghKrIWfn4ecHd3RHq6fiPz0Q1rI/lkHh5n6t7P09UR7ZoE6xuiXkRRxC/7dP/iKohAdkoeFr7YB9FRtU0XGBHZvKVLl+LOnTvIzc1Fq1atKmzTtm1bTJgwAd9++y0yMzOxefNmjB8/3ryBPoWJPJEN8wv0wsLVk/DexO+Rmqx+frJPTU8s+H4CvP/ZHZaqBrlcij49G2L9xr917iMIwKB+TSHxsMMPW3XvN6hLQ9jJTfuRciPhEeITH+vdb0/sFSbyRGZk7vKTISEhmDVrlt79XFyerDXz8PCAh4f2q4ojR47Et99+CwA4evQoE3kiWyCKItJTs5GTnQ9HJ3t4eDlbzSKtkIia+GbHLOze8Dd2rD+G5HtPEh3/IG/0HdUaPYa35JSaKmpg/6bY8usp5GkpA1isc8dw1KjhjnEDWuHkxQScuXpPa5+Gof4YP7DiESxjSjVwzv9jA/sRkW0IDAzE5MmTzXIuf39/uLm5ISMjA/fv67eDsSkwkadKQxRFnIuLx7bNJ3H5wj0o8pVwc3dCm4710XdQM/gYMBqdm5OPfdtPY9tPf+P2tSfVP2rX9UW/51qha//GcHSyN+avYRA3T2eMmNoFQyd1QtLdVORk5cHJ1QE1alWDRMINnKsyXx83fDBvEOZ9sFlrydKw+n6YNbMngKL59V/NHoz53+zCoZM31Pbp0CwE86f1Nng+vj7s5IbNaTe0HxEZQASgMtKQvJVuLOXg4ICMjAwolboNkJgSE3mqFFIeZuKDORtx+ULZ0cPHqdmIv/UQG9b8haHPt8aEl7vpXGruXvwjzJu2Gol3U8sdu309Ccs++hWbVh3Ggm/GIaC2dWzlLJVK4G8lsZD1aNkiGIsXPYelX++psBylXC5Fj64N8PK0rnAstaGTk4MdPnt9IC7fSsKmPWdw4uIdZOfkw8nRHi0a1MLQbo0RVsd8NcBr+1eDXCaBUo9KTQAQGsjKNERmZaUJuDrp6ek4ffo0UlNTERwcjEaNGqltW1hYiLS0olKqXl5e5gpRLSbyZPMep2ThjamrkHhP/dxZlUrEz+uOIjs7HzPf6qt1WkzKwwzMmfgDHj5I19guMSEVcyZ+j3//OA1ePpZduU6kSVSDACxfNg6XriRi/4FLSEnNgp2dDMG1q6Nn90i4uzup7RtWxxfvTLL8QmkPF0d0bRGK3Ucva2/8D6lEggEdGpgwKiKydffu3SuZmtO7d28sWbJEbdvz58+XbBwVGWn5XaGZyJNNykzLwR9bT+GvPRdw88oD5OYqIEglEO2kgFT9VJKdW0+hWcsQtO8SrvH51/5nr9YkvtijpAysXrYHb3w4RK/fgcjcBEFARJg/IsL8LR2KwZ7r2RS/H7sClY47R/aMDoO3h2GbpxGR/gQYb7GruVai1atXDx4eHkhLS8PBgwfx+PFjeHp6Vth25cqVJfetoY48J8+STRFFET9/fwhjOn+K7z7biYun4pGXnQ9BJUKiLIQ0WwFJtkLj/LyYn2M1niMrIxf79KjNDgAHdp1Fpp4l/ohIfxF1auDNsV10ahtexxezX9CtLRFVXXK5vGSn1pycHMyfPx+FheXXFK1btw67du0CALRv3x4NGzY0a5wV4Yg82ZTVS37Hhu8Oqj0uAhAKVZDkKKBysgMqmA9/Ni4e9+6koGZgxXPbjuy7BIWOFT6KKfKUOLrvInoMbqZXPyLS37CujeDmbI8l6w/iUVr5PRQkgoAerevjrXHd4FRqzj8RmYmOV8ysydSpU7Fnzx7cunULu3fvRmJiIl544QUEBgYiOTkZMTEx+P333wEAvr6+WLBggYUjLsJEnmzGyb+ulSTxokoF5OUD+flPRt9lMsDRAaJcBkEFCHlKiE4Vf4jfS0hVm8inPlJfj12TlIcZBvUjIv31aB2GLs3r4WDcDew7fg2PM3NgL5chvLYvBnaMgq+Xq07Pk52rwMVricjKyYezox0i6vrBxdnylaiIbJZoxDryZvw+4OLigpUrV+Lll1/GhQsXcObMmQpr09etWxfLli2Dn5+f+YLTgIk82YyYtUcAAGJuHpCVVf4FXlhYlNjLZBDdXSEAEFVihaPyhYXqq15INcyx10TG7d6JjEoURZy8cQ8xxy/gfmoGAAG1vN0xuFUkGgbVgEwmRdcWoejaIlTv576XlIaftp3A7oMXkZOrKHncwV6GHu0j8Fz/5giqWc2Ivw0RWTs/Pz9s2LAB27Ztw44dO3DhwgVkZmbC3d0ddevWRe/evTF06FDY2VnPlT4m8mQTHiam4cSfVyDm5AJZ5S+ll1FQADxOAzw9ICgLIdqX/zP38VVfUz4oxLBSdYEG9iOi8i7eTcK89b/hemJKmcePX7+LzcfOo0EtX3w8uieCffUv/3b64l3M/nQrsnPyyx3Lyy/Ar3vO4vc/L2HhvwaiVePahv4KRFWX7c2sKSGXyzFkyBAMGWIbBSy42JVsQsKtR1ApCrQn8cVUIpCRCVQw8h5c1xfB9Squfa1QFAAC4OqhvhRfRarXcEfztvX06kNEFTtzOxEvLvu5XBJf2oW7SRj77w24ev+RXs99OyEF//pkS4VJfGl5+UrM/Wwrrt4sX3efiMhaMJEnm1BYqAJy9awKoyyAoCi/aLXf0Obl6shnpudg5f/9gbE9F+O9V9bpXYGm/3OtIeXUGqJnlpOvwMzvY5BbwWv3aZm5+Xj1+xgoK6guoc73G4+UmUqjSb6iACt+OqzzcxNREUEUjXIj7ZjIk01wdrYrWtyqJzE3r8zPzVoFo1f/xmUeS05Mw2svfIcNP/yJ9Mf/jPhr2TCqtFadwjB0XFu9YyOi8naevIzULN2/SN9PzcD+czd0avvocRYO/n1Nr3iOxd3CvaQ0vfoQVWkiAJWRbszlteIcebIJDg4G/qmWGqlr3S4UcxcMKbMoNS9XgXkvr8W9+Kcu4QsCIJEUldBSMyogkQjoNbQ5ps3tx9F4IiP5+eg5vftsPHIWPRprX/B69NQtjQvdKyKKwOHjNzCyn+2Vlk3JzMHWY+ex69QVPMrMhlwqRV0/Lwxr0xAdGwRDZuDCfiKyHkzkySaoCg37Wi4AaNs5HP2HNkfjZrXLTan5PSYOd24+VNNZKLoVJ/OiCEEioF6EP5pG10PfES1QvYaHQXERUXkqlYgr99S8HjW4el+3PhmZhm3alm5gP0sRRRFrD5zC/20/DOVTX1yS07Nw5HI8ArzcsWTCANTz97ZQlFR5GXNaDIfktWEiTzbBq4b6KjOatOsRiXmfDK/wmCiK2L5R8y6vAJ4k9Ch6S+kxuDn6jWhpUDxEpF6hqILKgARAUaDbHHk7O8M+8hzs5Qb1s5Tv9xzH0h1/aWyTkJKOF5duxKpXR6CuH5N5IlvF62pkEzyqu6Fpp3C9+3Ud3lrtsaT7aepH4zX4++AVvfsQkXZyqRTuTg569/Ny1a3KVGSoYRu4NKhnHRu/6OLi3SStSXyxzNx8zF27CyIXFZKxiUa6kVZM5Mlm9H+ps17tfQKqoWWPhmqPZ2YYdrk808YusxPZkp5N9N/cqWfj+jq1CwupgfrBFZeeVSeoZjU0jayld0yWsv7P03q1v3r/EeJu3jNRNERkakzkyWa06tUQ7QY01amtRCrBzK/Gatyl1cHRsJ3ZDO1HRNqNaNNIr/YSQcCw6Cid2gqCgDGD9JsWN3pgi3Jra6xVTr4Cv53S/4rhpqPnTRANVWml1pY90420YiJPNkMikWD21xPQcVBzje3sHOR4d+VUNOvcQGM7v5qe8PR20TuOBo0D9e5DRLoJ9ffGqPaNtTf8x6TuLeFfzU3n9l3a1Me4oeqn3JU2om8z9OkcqfNzW1pSWhbydVwvUNqdhyyvScYjiMa9kWZM5Mmm2DnIMee7SVi46TW07dcEklIj7p4+bhj9Zj/8cPwjRPfRngjI5FL0HqJfSTmJVEDvIZq/SBDRs/nXoI4Y0lp7Av1Cp2aY3ita7+ef/Hw7vDWlB6pXq/iLfDUPJ7z2Uhe8Or6TzYzGAzBoofCz9CMiy2PVGrI5giCgaccINO0YgbycfGSkZsHOXg43LxdIJPp9N+07rAVi1v+N7Mw87Y0BdB/QBN6+uo/+EZH+pBIJ3h/RDd0a1sNPh0/jz0u3Sq6ySyUCOkeG4Pn2jdGiruFz1wd0b4g+XSJx+Ph1xJ6JR05uPpwc7dA0MhAdW9aDXG57e0NUd3OGVCKgUKVfYu7n6WqiiKjK4pdDs2EiTzbNwckeDk72Bvf38nHD/CWj8N4r/0NujuZt2xu3rIOX5/Q1+FxEpDtBENAuvDbahddGSmY2ktKyAAB+nm7wdHE0yjlkUgk6tQ5Fp9b6L7C1Rm5ODugYGYJ9Z6/r1a9/ywgTRUREpsapNVTlRTWrjcUrJ6JRizoVHndytsewcW3x4bKxsLOxetJElYGXqzMiavkiopav0ZL4yuq5dvotFq7p5YZ24bVNEwxVWYLKODfSjiPyRACC69fAou9exJ2bD3Hwt3N4nJIFuVyGOqG+6NQripVqiMgmtKxXC8PaROGXI+e0tpVLJfjw+Z6Q6jklkUgjEcabWsMZOloxkScqJTC4OsZO62LpMIiIDCIIAt4e1gUyqRQ/aagp7+Jghy9e7IfmdQPMGB0RGRsTeSIiokpEKpFg7tDOGNSqAX7+6wx2nbqCnHwlACCouieGtYnCwJYN4O6s/y66RDrhSLrZMJEnIiKqhMIDfPDeyO54d0Q35CqUkEulkMtsrxqPLpIfZuJBUjoAwK+GO6p7sxIPVQ1M5ImIiCoxQRDgZF/51vmIooi/jl3H1m1xOBF3u8yxFs3qYHD/JohuGWJTewFUDiIEo5Wf5NC+NkzkySpdOZ+AXZtO4Pb1ZBQWFKKatys69WmIdt0iIJfzz5aIqCorKFRh8b9/w64/Kl7Ue/zkLRw/eQv9ejXE6zN6QCrlgl6zYh15s2FGRFYl6X4aFs39GRdP3yl37O9DV7C8mjNemTcAbbuy7jERkbkVFKpwNPYG9hy8hJTULEilEgTV8kL/Xg1RL9jXbHEsW75XbRJf2vbdZ+HgIMeMKV3NEBWR+TGR10AURezduxcxMTE4d+4cUlJSIJPJ4O/vj+joaIwdOxZBQUGWDrPSSLqfhjdeWIGUh5lq26SlZuOjWevx5kdD0bVfYzNGR0RUtcWeuoUvlv2OpIcZZR4/ff4uYnadRuPIWnjnjT7wqW7a3a9vxT/C1m1xOrf/ZetJDOjbGIEBXiaMikqIAIxVA54D+1rxWpMaGRkZmDhxIl5++WX8/vvvSExMhEKhQE5ODq5fv461a9eib9++WLdunaVDrRREUcSnczZqTOKftAW+en8L7t9JMUNkRER06MhVvPXBpnJJfGmnz9/F9Nk/IilZfRtjiNmuexJf7Ncd6ktxEtkyjshXQBRFvPLKKzh27BgAIDIyEqNHj0ZISAjy8vJw9OhRrFmzBtnZ2fjwww/h4uKCgQMHWjhq23blXAIunbmrc/uCAhW2b4zF5Dd7mzAqIjI1hbIA+09ex76T1/A4Mwf2chnCa/tiUMco+Hu7Wzo8ApCUnIEFX2yHSqV9ePTho0zM/+xXfP35aJMtMj14+IrefQ78eYXTa8zIeItdSRsm8hXYuXNnSRLfq1cvLF68GDLZk/9VrVq1Qr9+/TBq1Cikp6dj4cKF6Nq1K1xcXCwVss3b+csJvfv8vvUUXpzZnYtfiWzUzqMX8e8Nh5CakVPm8WMX4rFqZyy6taiPt8d1g4ujvYUiJACI2X0aCmWhzu0vXknExSuJaBDmb/RYRFFEWnqO9oZPSU/PNXosRNaAU2sqsGnTJgCAg4MD5s+fXyaJL1a3bl1MmzYNAJCWloaDBw+aNcbK5tb1JL37ZGXmISVJ+1QcIrI+G/fG4f3vdpdL4ouJIvBH7BVM++xnZOXmmzk6KlZQqMKO38/q3e/XXaaZyiIIAuzs9B+8sbOrnPXzrZYoGudGWjGRf4ooijh16hQAoGnTpvD09FTbtk2bNiX3L1++bPLYKrPCAt1He0pTFhQYORIiMrXzNxLxxY/7dWp7OT4Zn/9vn4kjInUep2UjzYDR7Bu3H5ogmiL1QvSvjlPXgD5kIGMl8UzmdcJE/ikKhQLTp0/HmDFj0K1bN41txVJ/YPn5HDF6FtUM2IVPEADPapzORGRrftpzSq/P59/+voKHj7NMFxCppdRjSo0x+uliQB/9K5YN7MsqZ1Q5cXLxU+zt7TF58mSd2sbGxpbc9/c3/lzAqqRT7yic+OuaXn1atAuFi5ujiSIiIlNIy8zF3hP6vdYLVSr8evg8JvRvbaKoSB0PNycIgv4Do9U8nU0TEICO7evjv6sPIVmHKmcAUMPXHe3bhJosHqqAscpPklYckTdQfn4+Vq9eXfJz+/btLRiN7evQIxJuHk569ek3spWJoiEiU7l5PwUFhfp/yl+9Y7qpGqSek5MdWjcP0btf5/ZhJoimiL2dDAvnD4WLs/ZF0K6uDlg4fwjkcs6Rp8qJibyBFi1ahISEBABAhw4dEBKi/xsdPWFnL8fLc/vp3L5dtwi0aFfPhBERkSkoDVwPozCwHz27QXpOZXF2skO3juEmiqZI3WAfLFs8GuH1/dS2aRDuj/98ORrBtaubNBYqS0BR+Umj3Cz9y9iASje1Zv/+/Zg6dare/WrWrIl9+3RbULVq1aqSjaCcnJwwd+5cvc9H5XXsFYW8XAX+vSAGqkL113HbdYvA7IXDTFajmIhMp5qbflfeinkZ2I+eXatmddAhuh4OHdVtStSMSV3g5Ghn4qiA2kHe+GbJWFy+mojdf5zHg6R0AIBfDQ/07hGJ0Lo1TB4DVUCE8Rapcq2rVpUukTe1tWvX4tNPPwVQVAbro48+QnBwsIWjqjx6Dm6GiMaB2LYhFnu2xSEnq2gRsSAAzdvWQ9/hLdGyQygkEl5MIrJFdQO8UduvGm4npurVr3vL+iaKiLQRBAHzZvXFh19sx+Fj1zW0A16Z1BV9ukWZMTogLNQPYaHqR+aJKrNKl8iHhIRg1qxZevfTZTOnZcuWYenSpSU/z5kzB3379tX7XKRZrTrVMX1OX0x8vQceJWVAqSxENW8XuLpzRI7I1gmCgGGdG+lcfhIAAn090CI80IRRkTb29nIsmDsIh/++hq074nDyzJ0nx+xk6NYxHEP6N0XdOj4WjJKsBstGmk2lS+QDAwN1rjqjK6VSiffeew+bN28GUPRBNHfuXIwbN86o56Gy7Ozl8A/0snQYRGRkAztEYseRi7h0W/tGcBKJgH+N7gKJhFPpLE0iEdAhOhQdokOR8jgLKanZkMkkqFHdHU5Opp9KQ0TlcX6CFpmZmZg4cWJJEi+TybBw4UIm8UREBnKwk+Pfrw1GRB3Nm/TIZVJ8PKUvWkfWNk9gpDMvTxeEhvgiOKg6k3h6ijE3g+LIvjaVbkTemB49eoSXXnoJV65cAVC0sHXJkiXo2LGjhSMjIrJtnm5OWDFnJHYdvYSf950uU17S2dEO/dpEYHjXJgiqoX53bSKyQiKMV0eeebxWTOTVePz4McaNG4fr14sW9nh5eWH58uWIijLvIh4iosrKXi7DoA5RGNg+EvcepiM9Kxd2chlq+XjAwV5u6fCIiKweE/kKqFQqvPrqqyVJfM2aNbFy5UoEBQVZODIiospHEAQE+HggwMfD0qEQkREIXOxqNkzkK/DDDz8gNjYWAODh4YE1a9YgICDAwlERERERET3BRP4pWVlZWLFiRcnP48ePR2ZmJi5duqSxn7u7O/z9/U0dHhEREZF144i82TCRf8ru3buRnp5e8vOSJUuwZMkSrf0GDx5cslEUEREREZGpMZF/ysWLFy0dAhEREZHtUnFE3lyYyD/lvffew3vvvWfpMIiIiIhsj1Hrv4sA94LTiBtCERERERHZII7IExEREZHxGGuxK0fjteKIPBERERGRDeKIPBEREREZD0fkzYaJPBEREREZhwjjVa0RwGReCybyRDrKzc7H/l9P4czRG8jJzoezqwOatgtFx36NYe8gt3R4REREVMUwkSfSQqVSYf1/9mLz9weRk5Vf5tjB7afx3Sfb8Pz0rhj8UgcIAocOiIioKhMBUWW85yKNmMgTaSCKIpa8/Qv++OW42jZZ6bn47pPteJiYjsnv9GcyT0RERGbBqjVEGmxbe0RjEl/a1lV/Yn/MKRNHREREZOVE0Tg30oqJPJEahYUqbFl5SK8+v/z3IES++RARUVVVvNjVGDd+nGrFRJ5IjbjDV/HgbqpefW5dTsTluHgTRURERET0BBN5IjWunU8wsN89I0dCRERkK4w0rUYUwSF57ZjIE6mhyC8wrJ9CaeRIiIiIiMpj1RoiNdyrORvYz8XIkRAREdkQrhUzG47IE6kR3T1S71KScjsZWnYKN1FERERERE8wkSdSw7emJ1p21i8p79C3kcEj+URERJUCy0+aDRN5Ig1efLM3HJ3tdWrr5umM0a90N3FEREREVkwUAZXKODcm81oxkSfSICi0Bj7870twdnPQ2M7DywUfrZwIv0AvM0VGRFWZSiUiKS0Tt5JSkZKRbelwiMhCuNiVSIvIFsH4evsb+HXNX/j9l+PITMspOebh5YJeI1uh/5g2qObjZsEoiagqSMvOxeaj5/HzX2dxPzWj5PEGtXwxvF1D9GkWBns5P9rJwjiSbjZ8tRPpwMffExPn9MPY13ri1uVE5GbnwcnVEXXq+8HOni8jIjK98/EP8MqKrUjNyi137MLdJFxY/wfWHzqNpZMHwdeD1bOIqgJmIER6sHeQI6xxoKXDIKIq5uaDFEz9ZjMyc/M1trty7yGmfrMJq2eOhJuT5imBRCbDEXmz4Rx5IiIiK/fF1kNak/hiNx+kYs3+kyaOiEgNEYBKNM6N3we0YiJPRERkxe4+SsNfl27r1WfTkfNQFhSaJiAishpM5ImIiKzY7lNX9O6TmpWD2Gt3TRANkTYiRFFllBuH5LVjIk9ERGTFHqYbVl7S0H5EZDu42JWIiMiKyaSGjbkZ2o/omRTPkTfWc5FGTOSJiIisWF0/wzaaCzGwH9EzY9Uas+HXdSIiIivWs0l9ONvb6dWnQaAvwgN8TBQREVkLJvJERERWzNnBDkOiI/XqM7pjExNFQ6SNCKhUxrlxbo1WTOSJiIis3Iy+bdEk2F+ntsPaRKFPszATR0RUdcTExKB+/fqYMGGCTu1FUcSvv/6KcePGoWXLloiKikKXLl0we/ZsnD171qixcY48ERGRlXOwk+HrqUOwYMMe7Dp1ucIpyHYyKcZ3bY5pvaIhCIL5gyQCigbRjTVH3goG5O/evYuFCxfq3D4vLw8za9j5agAAEkdJREFUZ87EgQMHyjx+79493Lt3D9u2bcNrr72GKVOmGCU+JvJEREQ2wMlejk9e6I3pfaLxy19nEXfzPnLylXB3dkCHBnUwsFUDeDg7WjpMokojKSkJ48ePR1pams595s2bV5LER0dHY8yYMfD29saFCxewfPlyJCUl4csvv4Svry8GDRr0zDEykSciIrIhtbw98PrADpYOg6hCIkSIKpXRnstS4uLiMHPmTCQlJenc59ixY9i2bRsAoHfv3vjqq69Kro41btwYPXv2xMiRI5GQkIBFixahe/fucHZ2fqY4OUeeiIiIiIxHFI1zs4C8vDwsW7YMY8eORVJSEiQS3VPlVatWAQDs7e0xd+7cclPcvL298c477wAAUlNTsXXr1meOl4k8EREREVV58fHx6NWrF5YuXQqlUgkXFxd88803OvXNycnB4cOHARRNqfH19a2wXZcuXeDlVbTHw+7du585ZibyRERERGQcxTu7GuNm5kH5Bw8eIDExEQDQqVMnbN++HZ06ddKp77lz56BUKgEALVu21Ni2efPmAIqm7ygUCsMDBufIExERERFBEAQ0bdoUM2bMQNu2bfXqe+PGjZL7QUFBGtvWqlULAKBUKhEfH4969erpH+w/mMgTERERkZGIgGicxa7mHpJv3rw51q9fb1Df0oti/f017/ng5+dXph8TeSIiIiKyPBEQVbZZR16fha1PS09PL7mvrRKNo+OTMrEZGRkGnxNgIk9ERERENmz//v2YOnWq3v1q1qyJffv2GSWG0nPd7e3tNbZ1cHCosJ8hmMgTERERkfEYbWqN7ZBKpSX3te2sLJYqrfksVwEAJvJEREREZMNCQkIwa9Ysvfu5uLgYLQYnJ6eS+/n5+Rrblj5uZ2f3TOdlIk9ERERERiIab468jpPkAwMDMXnyZCOd0zCl58Xn5ORobJubm1ty393d/ZnOyzryRERERGQcIoqm1hjlZulfRnelK9U8ePBAY9viWvUA4OPj80zn5Yi8GcXHxwMALl26hLFjx1o4GqKq6/bt2yX3S9f+JSKyNZcuXQLwJMewNIV9LpJqGud9VWGfq72RlShdQvLOnTsa2969excAIJfLtdac14aJvBkVX2rJzMxEbGyshaMhIgBITk62dAhERM9M23QOcxGlKuQ7ZVs6DLOrX78+HB0dkZubixMnTuCFF15Q2/bEiRMAgMaNG0Mme7ZUnIm8GQUEBCAhIQFOTk7P/A2MiIiIKD4+Hjk5OQgICLBoHGFhYTb53Mbi4OCADh064LfffsOhQ4eQkpICLy+vcu327duHlJQUAEC3bt2e+byCWLoGDhERERERlahfvz4AoF27dvj+++/VtouNjS2ZOt2lSxcsXbq0zIj7o0ePMHLkSCQkJMDd3R179uyBm5vbM8XGxa5ERERERM+oZcuWGDBgAICikffRo0dj165dOH36NNavX48hQ4YgISEBADB79uxnTuIBTq0hIiIiIjKKjz76COnp6Th48CBOnz6N1157rcxxQRAwY8YMDBs2zCjnYyJPRERERGQE9vb2WLFiBbZt24YtW7bg4sWLyMrKgoeHB5o1a4YXXngBzZo1M9r5OEeeiIiIiMgGcUTeComiiL179yImJgbnzp1DSkoKZDIZ/P39ER0djbFjx7LqjQ2LiYnB7NmztS6aIfNSKpXYuHEjtm3bhmvXrkGpVMLX1xdt27bF2LFjERISYukQyUAKhQJDhgzBtWvXsGHDBjRu3NjSIZGOkpKS8OOPP+Lw4cO4c+cOcnNz4e7ujvDwcPTt2xf9+/d/5vJ9RLaMI/JWJiMjA6+//joOHz6sto1cLsfcuXMxevRoM0ZGxnD37l0MGzYMaWlpTOStyOPHjzFp0iScO3euwuP29vb44IMPMHjwYDNHRsbw6aefYuXKlQDARN6G7Ny5E++8847G+uhRUVH4z3/+A19fXzNGRmQ9+DXWioiiiFdeeQXHjh0DAERGRmL06NEICQlBXl4ejh49ijVr1iA7OxsffvghXFxcMHDgQAtHTbpKSkrC+PHjkZaWZulQqBSVSoVXXnmlJInv1asXhgwZAldXV5w8eRLLly9HZmYm5s2bBz8/P7Ru3drCEZM+li9fXpLEk+04evQo3nzzTRQWFsLe3h6jRo1C+/bt4erqirt372L9+vU4fvw4zp07h0mTJmHDhg1wdHS0dNhEZscReSuyY8cOvPHGGwCKkonFixeXu2R4/fp1jBo1Cunp6fDw8MDevXvh4uJiiXBJD3FxcZg5cyaSkpJKHuOIvHXYtGkT3n77bQDASy+9hLfeeqvM8Rs3bmDUqFFIS0tDaGgoYmJiIJGwcq+1UygU+Pjjj/HTTz+VeZwj8tZPFEX06dMHN2/ehL29PdasWVPu30wURcyfP7/k3/f111/H1KlTLREukUXx08iKbNq0CUDR7mDz58+vcN5f3bp1MW3aNABAWloaDh48aNYYST95eXlYtmwZxo4di6SkJCaAVmjVqlUAAG9vb8ycObPc8ZCQEMyYMQMAcPXqVRw6dMic4ZEBzp49i+eff74kyZNKpRaOiPQRFxeHmzdvAgDGjh1b4RcvQRDw9ttvl+ycuXXrVrPGSGQtmFVYCVEUcerUKQBA06ZN4enpqbZtmzZtSu5fvnzZ5LGRYeLj49GrVy8sXboUSqUSLi4u+OabbywdFpVy+/ZtXL16FQDQs2dPODg4VNhu8ODBJcng7t27zRYf6e+LL77AiBEjcP78eQBA165dMW7cOAtHRfo4ceJEyf0uXbqobWdvb19Sxu/WrVtQKBQmj43I2nCOvJVQKBSYPn06kpKSEBwcrLFt6dlQ+fn5pg6NDPTgwQMkJiYCADp16oT58+fDz8/PwlFRacVfnoGiHfnUcXFxQVhYGC5cuFCyhoWs05kzZyCKIjw8PPDmm29i+PDhWLp0qaXDIj00bNgQU6ZMQXJystYKbU9/HtrZ2Zk6PCKrwkTeStjb22Py5Mk6tY2NjS257+/vb6qQ6BkJgoCmTZtixowZaNu2raXDoQrcuHGj5H7t2rU1tg0MDMSFCxeQmJiI7OxsODs7mzg6MoSbmxsmTZqESZMmwd3d3dLhkAFat26t06JypVJZ8mXc1dUVrq6upg6NyOowkbcx+fn5WL16dcnP7du3t2A0pEnz5s2xfv16S4dBGpRefKztaknp48nJyahTp47J4iLDLV26lGtRqohNmzYhJSUFQFHxAKKqiO92NmbRokVISEgAAHTo0IGb1FgxJhPWLz09veS+thH20qXtMjMzTRYTPRu+7qqG+Ph4LF68uOTnF1980YLREFkOR+SNZP/+/QaVvqpZsyb27dunU9tVq1Zh3bp1AAAnJyfMnTtX7/ORdub4tyTrULw4TiqVat0dsvRCWC6qI7KclJQUTJkyBRkZGQCA4cOHo1GjRhaOisgyOHRhI9auXYtPP/0UQNHc648++kjrolgi0qy4Eo0gCFrbll5Up0t7IjK+hw8fYvz48bh16xYAICIiAvPmzbNwVESWwxF5IwkJCcGsWbP07qfLZk7Lli0rU3Vhzpw56Nu3r97nIt2Y8t+SrIuTkxMAoKCgAIWFhRrrjZeuEMXKGETmd+fOHUyYMAF37twBANSpUwffffed2rKxRFUBE3kjCQwM1LnqjK6USiXee+89bN68GUDRKODcuXNZE9nETPFvSdap9Lz43NxcjV/GcnNzS+57eHiYNC4iKisuLg7Tp09HamoqAKBevXr44Ycf4O3tbeHIiCyLU2usVGZmJiZOnFiSxMtkMixcuJBJPJERlS7fWlzzX53i44IgoHr16iaNi4ie2LVrF8aNG1eSxDdq1Ahr166Fj4+PhSMjsjwm8lbo0aNHGD16dMnGM05OTvj6668xZMgQC0dGVLnUq1ev5H7x5Xp1io/XrFmTl/KJzOTHH3/EG2+8UTK1rVOnTli9erXG3c+JqhIm8lbm8ePHGDduHK5cuQIA8PLywpo1a9CxY0cLR0ZU+TRs2LDkfult4Z+WlZWFy5cvAyjaH4CITO/HH3/EBx98AJVKBQAYMWIEvv766zKlYImqOibyVkSlUuHVV1/F9evXARSN/K1fvx5RUVEWjoyocgoICEBkZCQAYMeOHWrLSm7ZsgWFhYUAgO7du5stPqKq6siRI1iwYEHJz1OnTsWCBQs0LkgnqoqYyFuRH374AbGxsQCKFtOtWbMGQUFBFo6KqHIbM2YMgKJdXotLvJZ248YNLFu2DAAQFBSETp06mTM8oionMzMTb731VslI/Pjx4/H6669bOCoi68SqNVYiKysLK1asKPl5/PjxyMzMxKVLlzT2c3d3L7Ngj4j0M2jQIPzyyy84ceIE1q1bh7t37+L555+Hh4cH4uLi8O233yIjIwMSiQTvv/++1o2jiOjZrF27FsnJyQCKrkz369dP62chUFQ6mKVhqarhJ5KV2L17d5nt4pcsWYIlS5Zo7Td48OAKRxGJSDeCIGDZsmWYOHEizp8/j0OHDuHQoUNl2sjlcsyfPx9t27a1UJREVcfGjRtL7t+7dw/Dhg3Tqd/evXsREBBgqrCIrBITeStx8eJFS4dAVGV5enpiw4YN2LhxI7Zv347r168jJycH1atXR+vWrfHiiy8iNDTU0mESVXqpqalaS8ES0ROCWHrfcSIiIiIisglc7EpEREREZIOYyBMRERER2SAm8kRERERENoiJPBERERGRDWIiT0RERERkg5jIExERERHZICbyREREREQ2iIk8EREREZENYiJPRERERGSDmMgTEREREdkgJvJERERERDaIiTwRERERkQ1iIk9EREREZIOYyBMRERER2SAm8kRERERENoiJPBERERGRDWIiT0RkBuPHj0f9+vVRv359bN++XW27wsJCTJ06taTtokWLzBglERHZEibyRERmMGvWLAiCAABYunQpCgsLK2w3f/587N+/HwAwYMAAzJ4922wxEhGRbWEiT0RkBlFRUejZsycA4Pbt24iJiSnXZunSpdi4cSMAoF27dli4cGFJ8k9ERPQ0QRRF0dJBEBFVBbdu3UK/fv1QUFCAgIAA7N69G3K5HADw888/Y968eQCAhg0bYvXq1XBycrJkuEREZOU4Ik9EZCZ16tTB0KFDAQAJCQnYtGkTAGD//v14//33AQC1a9fG8uXLmcQTEZFWHJEnIjKjpKQk9OjRA3l5eahRowY+//xzTJ48Gbm5uahevTp++uknBAQEWDpMIiKyAUzkiYjM7IsvvsB3330HAJBKpSgsLISrqyv+97//ISwsTG2/8+fP48iRIzh79izOnDmD5ORkAMCVK1fMEjcREVkXJvJERGaWkZGBbt26IT09HQBgZ2eH77//Hi1bttTYb/r06di7d2+5x5nIExFVTTJLB0BEVNUkJiai9BhK7969tSbxANC4cWOEhYUhKioKUVFR6NChg9oylkREVPlxRJ6IyIzu37+PkSNHlkyLAYpG5H/77Tf4+/vr9VwREREoLCzkiDwRURXFqjVERGby+PFjTJgwAcnJyZBIJBg0aBAAQKFQ4P/+7/8sHB0REdkaJvJERGaQm5uLqVOn4ubNmwCAd955B5988glCQ0MBADExMbh27ZolQyQiIhvDRJ6IyMQKCgrw2muv4fTp0wCAKVOmYMyYMZBIJHjllVcAACqVCl9++aUlwyQiIhvDRJ6IyMTeffddHDhwAAAwZMgQvPHGGyXHevTogQYNGgAA9u3bh1OnTlkiRCIiskFM5ImITGjx4sXYvHkzAKBjx45YsGBBuTYzZ84s056IiEgXTOSJiExk7dq1WLFiBQCgYcOGWLJkCWSy8lV/O3bsiCZNmgAATpw4gYMHD5o1TiIisk1M5ImITGDnzp1YuHAhAKB27dpYvnw5nJyc1LZ/elRepVKZPEYiIrJtrCNPRGSjWEeeiKhq44g8EREREZENYiJPRERERGSDyq+6IiIiq3TgwAF8/fXXJT8XFhYCAEaMGFHy2PDhwzF8+HCzx0ZERObHRJ6IyEakpqbizJkz5R4v/Vj79u3NGRIREVkQF7sSEREREdkgzpEnIiIiIrJBTOSJiIiIiGwQE3kiIiIiIhvERJ6IiIiIyAYxkSciIiIiskFM5ImIiIiIbBATeSIiIiIiG8REnoiIiIjIBjGRJyIiIiKyQUzkiYiIiIhsEBN5IiIiIiIbxESeiIiIiMgGMZEnIiIiIrJBTOSJiIiIiGwQE3kiIiIiIhvERJ6IiIiIyAb9P4m+qGsN/89HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 267,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(*D.T, c=y, cmap=\"viridis\")\n",
    "plt.xlabel(\"$x_1$\")\n",
    "plt.ylabel(\"$x_2$\")\n",
    "\n",
    "ymin, ymax = plt.ylim()\n",
    "plt.axhline(y=tree.info[0], c=\"tab:gray\")\n",
    "plt.vlines(x=tree.left.info[0], ymin=ymin, ymax=tree.info[0], color=\"tab:gray\")\n",
    "plt.vlines(x=tree.right.info[0], ymin=tree.info[0], ymax=ymax, color=\"tab:gray\")\n",
    "plt.ylim(ymin, ymax)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `RTree` so far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtree = RTree(D, y, 4)\n",
    "rtree.fit_subtrees()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A helper function to count the number of terminal nodes inside a non-pruned tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _count_terminal_nodes(subtree, hist=\"\"):\n",
    "    if subtree.left is None and subtree.right is None:\n",
    "        print(hist)\n",
    "        return 1\n",
    "    # we've reached a left terminal node; we continue on the\n",
    "    # left branch\n",
    "    elif subtree.left is None:\n",
    "        return _count_terminal_nodes(subtree.right, hist=hist+\"R\")\n",
    "    # we've reached a right terminal node; we continue on the\n",
    "    # right branch\n",
    "    elif subtree.right is None:\n",
    "        return _count_terminal_nodes(subtree.left, hist=hist+\"L\")\n",
    "    else:\n",
    "        return _count_terminal_nodes(subtree.left, hist=hist+\"L\") + \\\n",
    "               _count_terminal_nodes(subtree.right, hist=hist+\"R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tree(Node):\n",
    "    def __init__(self, data=None):\n",
    "        super().__init__(data)\n",
    "        \n",
    "tree = Tree(\"*\")\n",
    "tree.left = Tree(\"L\")\n",
    "tree.left.right = Tree(\"LR\")\n",
    "tree.left.left = Tree(\"LL\")\n",
    "tree.left.left.left = Tree(\"LLL\")\n",
    "tree.left.left.right = Tree(\"LLR\")\n",
    "\n",
    "tree.right = Tree(\"R\")\n",
    "tree.right.left = Tree(\"RL\")\n",
    "tree.right.left.right = Tree(\"RLR\")\n",
    "tree.right.left.left = Tree(\"RLL\")\n",
    "tree.right.right = Tree(\"RR\")\n",
    "tree.right.right.left = Tree(\"RRL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LLL\n",
      "LLR\n",
      "LR\n",
      "RLL\n",
      "RLR\n",
      "RRL\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_count_terminal_nodes(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ______*______ \n",
      "         /             \\\n",
      "      __L__           __R__ \n",
      "     /     \\         /     \\\n",
      "   LL      LR      RL      RR \n",
      "   / \\             / \\     /  \n",
      " LLL LLR         RLL RLR RRL    "
     ]
    }
   ],
   "source": [
    "tree.prettyPrint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LLL\n",
      "LLR\n",
      "LRL\n",
      "LRRL\n",
      "LRRR\n",
      "RLL\n",
      "RLRL\n",
      "RLRR\n",
      "RRLL\n",
      "RRLR\n",
      "RRR\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_count_terminal_nodes(rtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RTreev2(RTree, Node):\n",
    "    def __init__(self, X, y, depth):\n",
    "        RTree.__init__(self, X, y, depth)\n",
    "        Node.__init__(self)\n",
    "    \n",
    "    def fit_subtrees(self):\n",
    "        super().fit_subtrees()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "trv2 = RTreev2(D, y, 4)\n",
    "trv2.fit_subtrees()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  ______________*______________ \n",
      "                 /                             \\\n",
      "          ______*______                   ______*______ \n",
      "         /             \\                 /             \\\n",
      "      __*__           __*__           __*__           __*__ \n",
      "     /     \\         /     \\         /     \\         /     \\\n",
      "    *       *       *       *       *       *       *       * \n",
      "                           / \\             / \\     / \\        \n",
      "                          *   *           *   *   *   *         "
     ]
    }
   ],
   "source": [
    "trv2.prettyPrint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree Pruning\n",
    "In order to ahieve tree pruning, we consider another helper function that computes the MSE averaged over all nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leaf_func(subtree, f):\n",
    "    \"\"\"\n",
    "    Apply a cummulative function f applied over all\n",
    "    leaf (terminal) nodes\n",
    "    \"\"\"\n",
    "    if subtree.left is None and subtree.right is None:\n",
    "        return f(subtree)\n",
    "    # we've reached a left terminal node; we continue on the\n",
    "    # left branch\n",
    "    elif subtree.left is None:\n",
    "        return leaf_func(subtree.right, f)\n",
    "    # we've reached a right terminal node; we continue on the\n",
    "    # right branch\n",
    "    elif subtree.right is None:\n",
    "        return leaf_func(subtree.left, f)\n",
    "    else:\n",
    "        return leaf_func(subtree.left, f) + \\\n",
    "               leaf_func(subtree.right, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "part_mse = lambda subtree: subtree.info[1] * len(subtree.y)\n",
    "count = lambda subtree: 1\n",
    "count_samples = lambda subtree: len(subtree.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "depths = range(2, 15)\n",
    "tree_mse = []\n",
    "mean_samples = []\n",
    "for depth in depths:\n",
    "    tree = RTree(D, y, depth)\n",
    "    tree.fit_subtrees()\n",
    "    mse = leaf_func(tree, part_mse) / len(y)\n",
    "    nleaf = leaf_func(tree, count)\n",
    "    nsamples = leaf_func(tree, count_samples)\n",
    "    mean_samples.append(nsamples / nleaf)\n",
    "    tree_mse.append(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd55aea4e90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 267,
       "width": 437
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, host = plt.subplots()\n",
    "par = host.twinx()\n",
    "\n",
    "host.plot(depths, tree_mse, c=\"crimson\", label=\"depth\")\n",
    "host.scatter(depths, tree_mse, c=\"crimson\")\n",
    "par.plot(depths, mean_samples, c=\"tab:gray\", label=\"num. samples\")\n",
    "par.scatter(depths, mean_samples, c=\"tab:gray\")\n",
    "host.grid(alpha=0.6)\n",
    "host.set_ylabel(\"MSE\")\n",
    "par.set_ylabel(\"avg. samples\")\n",
    "host.set_xlabel(\"Depth\");\n",
    "host.legend(bbox_to_anchor=(0.835, 1))\n",
    "par.legend(bbox_to_anchor=(1, 0.9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "* https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/splits.pdf\n",
    "* https://github.com/jdmcpeek/pretty-print-binary-tree"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
